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Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities
Abstract
This paper discusses some trends and recent advances in model-based Fault Detection, Isolation and Recovery (FDIR) for aerospace systems. The FDIR challenges range from pre-design and design stages for upcoming and new programs, to improvement of the performance of in-service flying systems. For space missions, optimization of flight conditions and safe operation is intrinsically related to GNC (Guidance, Navigation & Control) system of the spacecraft and includes sensors and actuators monitoring. Many future space missions will require autonomous proximity operations including fault diagnosis and the subsequent control and guidance recovery actions. For upcoming and future aircraft, one of the main issues is how early and robust diagnosis of some small and subtle faults could contribute to the overall optimization of aircraft design. This issue would be an important factor for anticipating the more and more stringent requirements which would come in force for future environmentally-friendlier programs. The paper underlines the reasons for a widening gap between the advanced scientific FDIR methods being developed by the academic community and technological solutions demanded by the aerospace industry.
... In particular, model-based and data-driven (also called soft-computing) FDIR systems are introduced. As shown later, they can provide the capabilities of processing anomalous observations in spite of uncertainties and partial observability in order to estimate the system health status and determine the most appropriate corrective action [135,[139][140][141]. After that, the central part of this section presents advanced FDIR solutions for GNC applications. ...
... For instance, the traditional FDIR solutions are not able to cope with the partial observability of the system. In particular, simple diagnostic (thresholdbased) routines process symptoms in isolation, which may result in incorrect diagnoses or contradictory deductions [ 127,134,135]. One prominent example is the Mars Express mission: due to a nonresolvable memory failure, it suffered from repeated safe mode transitions, which resulted in a suspension of science operations [136]. ...
... Thus, even if the studies using model-based and datadriven methods both in the space sector and in other fields show encouraging results, there is still a lack of understanding of how these methods can be robustly integrated in on-board S/C FDIR systems. In Ref. [135], the reasons for such a widening gap between the advanced scientific FDIR methods being developed by the academic community and technological solutions demanded by the aerospace industry are discussed. Major problems can be found in the lack of an effective development process for maturing on-board implementations of advanced FDIR systems as well as verification and validation (V&V) aspects. ...
The following chapter gives an overview on modern techniques for guidance, navigation, and control (GNC). In particular, an overview of artificial intelligence (AI) techniques is provided in light of a tailored application to the space domain. Thanks to their
enormous success in a great variety of applications and fields, modern AI techniques can be found in almost every aspect of science and engineering as well as everyday life. AI enables the automation of tasks previously limited to humans, even surpassing human
performance on many tasks. Consequently, the terms AI, machine learning, and deep learning are nowadays ubiquitous and are often used interchangeably to describe computer systems which are designed to act in an intelligent way. Among the modern applications, a thorough description of innovative methods for GNC failure (or fault) detection, isolation, and recovery is presented, highlighting the latest novelties. Finally, the emerging topic of CubeSats and nanosatellites, in general, is treated by underlining the peculiar challenges that such missions pose.
... Depending on mission and system-level requirements, different FDIR approaches have been conceived, designed, and implemented. Generally speaking, the main objectives of a fault-tolerant GNC are [33]: ...
... Unit-level FDIR capabilities can be allocated into the GNC module itself, while mission-level FDIR can be provided by the MVM module. In this regard, if needed, mission objectives can change as a result of a mission-level recovery action, e.g., the reshape of a new reentry trajectory for a high or medium lift-to-drag ratio vehicle as reported by Ref. [33]. ...
This chapter presents technical solutions and industrial processes used by the Space Industry to design, develop, test, and operate
health (or failure) management systems, which are needed to devise and implement space missions with the required levels of
dependability and safety. The overall chapter is inspired by Failure (or Fault) Detection, Isolation and Recovery (FDIR) systems
designed for European Space Agency missions; however, the presentation is maintained at a proper level of detail so that its contents are in line with the FDIR practices adopted by other space agencies.
... Over the last two decades, special attention has been paid to switched affine systems because they can be used to effectively model a wide range of practical systems, such as chemical plants [1], aeronautic systems [2] and smart buildings [3]. These systems are usually difficult to be exactly described by a single model because of their nonlinear and complex dynamic characteristics. ...
... where the temperature of two cores in the radiant system is denoted by T c,i for i ∈ [2]. The temperature of the supply water is denoted by T w,i . ...
We study a diagnosis scheme to reliably detect the active mode of discrete-time, switched affine systems in the presence of measurement noise and asynchronous switching. The proposed scheme consists of two parts: (i) the construction of a bank of filters, and (ii) the introduction of a residual/threshold-based diagnosis rule. We develop an exact finite optimization-based framework to numerically solve an optimal bank of filters in which the contribution of the measurement noise to the residual is minimized. The design problem is safely approximated through linear matrix inequalities and thus becomes tractable. We further propose a thresholding policy along with probabilistic false-alarm guarantees to estimate the active system mode in real-time. In comparison with the existing results, the guarantees improve from a polynomial dependency in the probability of false-alarm to a logarithmic form. This improvement is achieved under the additional assumption of sub-Gaussianity, which is expected in many applications. The performance of the proposed diagnosis filters is validated through a synthesis numerical example and an application of the building radiant system.
... The general approach to fault detection, isolation, and recovery (FDIR) is designed to ensure the uninterrupted performance of spacecraft functions [1]. This approach involves ensuring redundancy, using automatic control systems that carry out diagnostics and start recovery procedures, and also thoroughly studying the identified failures and unforeseen situations and adjusting the flight plan appropriately. ...
... The authors validated this approach using data from Intelsat and Inmarsat telecommunication satellites and the SpaceTrak database. 1 Nozari et al. [90] proposed to analyze the results of various classifiers using a feedforward neural network and demonstrated the network operation on the support vector machine, the PLS (partial least squares), and a random forest using data on the operation of an FCM. ...
We survey the progress in data mining methods for spacecraft health monitoring. The main emphasis is placed on the analysis of telemetry data enabling the identification of spacecraft states that are atypical during normal operation and the prediction of possible failures in the operation of the spacecraft or its components. The main stages required for the creation of general-purpose spacecraft state monitoring systems are considered; methods for detecting anomalies in telemetry data taking into account the specific features of the spacecraft are presented in detail; and publications on this topic known to the authors are analyzed. Examples of the implementation of such systems in flight control centers of various countries are given. The promising areas of development of methods for analyzing the technical state of complex systems relevant for solving problems in space technology are discussed, and the main factors that hinder the development of machine learning methods for analyzing telemetry data are noted.
... Helicopters have a greater accident risk than fixed-wing aircraft because of their structural characteristics, complex structure, environmental factors, and nonlinear aerodynamics. Therefore, there are several difficulties involved in flight control, which affect performance and stability [5]. Research by the Rotary Wing Society of India found that from 2005 to 2014, there were 5.34 helicopter accidents for every 10,000 flight hours. ...
Fault detection and control of nonlinear helicopter systems is crucial in ensuring safety and reliability. The effects of faults on system dynamics become more challenging to control due to the complexity of helicopter dynamics, which exhibit significant nonlinearity and cross-coupling as well as external disturbances like wind, icing, and air turbulence that affect the system. The goal of this work is to provide an active fault diagnosis and control approach for a nonlinear two-degrees-of-freedom helicopter system when it is exposed to different abnormalities, such as sensor, actuator, and component faults. An integrated design of fault diagnostic and fault-tolerant control is designed using adaptive sliding mode control. The fault diagnosis is carried out using particle filter, and based on the state feedback from the particle filter, the controller gain is adjusted to provide fault tolerance. System state and error covariance are both used by the controller as state feedback. Simulations are carried out to show the effectiveness of adaptive super-twisting SMC compared to traditional super-twisting SMC. Results indicate that the adaptive SMC tracks the system states perfectly. The effectiveness of SMC algorithms is evaluated using a variety of performance indicators, and the results demonstrate a marginal improvement in controller performance over super-twisting SMC. Also a stability analysis is carried out using Lyapunov approach in the face of faults and the stability of the controller is guaranteed.
... In recent years, due to the increasing demand of higher performances, modern industrial systems are usually subject to faults. To enhance system reliability and safety, Fault Detection (FD) has been widely investigated in many engineering fields such as automotive [1], [2], aerospace [3], [4] and energy systems [5]. ...
n this paper, zonotopic fault detection methodology is proposed for a class of discrete-time Linear Parameter Varying (LPV) systems with sensor faults. The disturbances and measurement noise are assumed to be unknown but bounded by zonotope. First, a fault detection observer is designed based on L∞ performance to attenuate the effects of the uncertainties and to improve the accuracy of the proposed residual framers. Then, the fault sensitivity is taken into account by measuring performance and zonotopic residual evaluation is presented. Finally, the effectiveness of the proposed method is demonstrated by a numerical example.
... As FDIR has a long standing history in the space domain there exists a huge number of excellent literature about the topic in general, e.g. for single satellites [10,11,12,13,14,15,16] and whole formations [17], just to name a few. ...
In recent years modular satellite architectures have become more and more prevalent, thanks to their advantages in comparison with mission-tailored single use architectures. Fractionated satellite architectures go even further by increasing the subsystem autonomy and interconnectivity even more, e.g. by going from a wired satellite harness to a wireless one. If the OBDH of a fractionated satellite in an earth observing formation fails, the other satellites in the formation could take direct control of the broken satellite's attitude control subsystem and therefore the original mission objective could still be continued. However, this approach imposes different challenges to the satellites. First, the failure in a subsystem has to be detected and an appropriate replacement has to be selected and assigned. Second, if for example closed-loop control is performed via wireless connection links, the communication channel properties have to be taken into account in the controller design. This paper presents solutions for both described problems. The solution of the first problem requires regular intra-satellite communication e.g. via alive messages to recognize failures in subsystems. For the selection of an appropriate replacement subsystem, a bidding method from the multi-robot-systems domain is proposed. For the second problem of control via communication links, we propose a networked control approach based on a Lyapunov Model-Predictive Control (MPC) methodology. As demonstration example, MPC is adapted and applied to a satellite attitude control problem of a typical nano-satellite in LEO. Using a high-fidelity attitude-dynamic simulation, a comparison of stability and control performance of a traditional Lyapunov-based method and a networked MPC is performed. Figures of merit for stability and performance are presented.
... Many FDDs schemes have been developed mostly for monitoring purposes. Model based FDD schemes are predominantly used because of its fast speed of response. Model based methods are based on state estimation, parameter estimation or parity based methods which uses dynamic process or system models. See Isermann. (2005). Zolghadri. (2012) and Zolghadri et al. (2016) emphasizes the advantages of model-based fault detection methods to aerospace systems. ...
Fault diagnosis of non-linear helicopter systems are affected by inherent characteristics such as non-linear behaviour, high cross coupling effects, external disturbances such as atmospheric turbulence and wind effects. Fault diagnosis in non-linear systems gains importance due to its high complexity and this work focuses on fault detection of helicopter system with the consideration of the inherent non-linearity effects. This paper deals with the detection, identi�cation and classi�cation of sensor, actuator and component faults in nonlinear helicopter systems using model-based state estimation approaches. Approaches include
Interacting Multiple Model based Extended Kalman Filter and Interacting Multiple Model based Unscented Kalman Filter. To address problem of fault detection, statistical measures of
residual analysis, stochastic likelihood ratio and model probability is proposed. A Comparison of these approaches is presented based on the ability to detect, identify and classify faults in
spite of system non-linearity. Algorithm is applied to 2 degrees of freedom helicopter and the results for various fault cases are presented. The results yield better fault detection performance
using Interacting Multiple Model based Unscented Kalman Filter.
... As an important class of hybrid systems, switched systems have been the subject of many studies over the past two decades. Many industrial systems, such as chemical plants [13] and aeronautic systems [17], are difficult to be exactly modeled due to the nonlinearity and complexity of the underlying dynamics. Nonetheless, these systems can be effectively modelled by switched systems, see for example [7] and the reference therein. ...
... • Extension of the detailed FDIR design to Safe Mode • Comparison with other satellite control architectures and FDIR philosophies typically used in the industry [52,53] • Extension of the theoretical analyses (Section 4.2) and incorporation of parameters such as cost [54], complexity, past experience and further architectures ...
Space is not a welcoming environment; while the aerospace engineering community has managed to reliably operate thousands of satellites in orbit, CubeSats, the most popular class of nanosatellite, only have a 50% success rate. Low costs, lack of strict technical requirements and scarcity of publicly available documentation often drive up the risks for educational, scientific and commercial CubeSats. This thesis investigates a configurable and modular Fault Detection, Isolation and Recovery (FDIR) architecture that uses the ECSS Packet Utilisation Standard. This FDIR concept, along with the provided open-source software implementation, can be used by CubeSat missions to increase the reliability of their design and chances of mission success, by autonomously responding to on-board errors. The thesis also includes background information regarding CubeSat reliability, and explores the software and hardware used to implement the proposed FDIR design on the AcubeSAT mission, currently under design by students of the Aristotle University of Thessaloniki.
... The second purpose consists in minimizing ρ to look for optimum dwell time. The resolution of such a problem leads to solving a problem of linear optimization Introduction Ces dernières années, en raison de la complexité croissante des technologies industrielles, l'un des enjeux les plus importants concerne le diagnostic des systèmes complexes [208], [209], [210], [118]. Ainsi, les industriels accordent au diagnostic et à la maintenance un intérêt croissant et cherchent à mettre en place des procédures pour améliorer la sécurité des personnels lors de l'apparition de défauts, réduire les risques encourus et assurer la fiabilité des machines et des installations. ...
This thesis deals with state estimation and fault detection for a class of switched linear systems. Two interval state estimation approaches are proposed. The first one is investigated for both continuous and discrete-time linear parameter varying switched systems subject to measured polytopic parameters. The second approach is concerned with a new switching signal observer, combining sliding mode and interval techniques, for a class of switched linear systems with unknown input. State estimation remains one of the fundamental steps to deal with fault detection. Hence, robust solutions for fault detection are considered using set-membership theory. Two interval techniques are achieved to deal with fault detection for discrete-time switched systems. First, a commonly used interval observer is designed based on an L∞ criterion to obtain accurate fault detection results. Second, a new interval observer structure (TNL structure) is investigated to relax the cooperativity constraint. In addition, a robust fault detection strategy is considered using zonotopic and ellipsoidal analysis. Based on optimization criteria, the zonotopic and ellipsoidal techniques are used to provide a systematic and effective way to improve the accuracy of the residual boundaries without considering the nonnegativity assumption. The developed techniques in this thesis are illustrated using academic examples and the results show their effectiveness.
... According to Yu and Jiang (2015) and Zolghadri (2012), within the analytical redundancy category, model-based methods have been most frequently used in fault detection for critical applications requiring Fault-Tolerant Control (FTC) due to their low implementation complexity. Model-based fault detection is centered in the generation of residuals, which act as fault signals. ...
New computational models for designing fault-tolerant onboard software on satellites
... Modern FDD approaches usually rely on the model of the system. In this case, if a model mismatch were to exist, the performance of the FDD could degrade significantly [3]. Therefore, increasing the robustness to modeling uncertainty, whilst without losing fault sensitivity is the most crucial point in the model-based FDD concept. ...
This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD.
... Due to the increasing demand for higher performance and safety, fault diagnosis for dynamic systems is becoming a major technological challenging issue in many engineering fields, such as automotive [1] and aerospace [2,3]. Faults are generally unavoidable especially for complex systems and may lead to significant 5 performance degradation. ...
This paper deals with sensor fault detection (FD) for discrete-time switched systems subject to bounded disturbances. A novel approach is investigated to construct residual framers using interval observer with L∞ performance. The proposed technique is used to provide more degrees of design freedom and to obtain accurate FD results. The design conditions of the FD observer are given in terms of Linear Matrix Inequalities (LMIs) adopting firstly a common quadratic Lyapunov function, under an arbitrary switching signal and secondly multiple quadratic Lyapunov functions, under an Average Dwell Time (ADT) switching signal. Furthermore, the FD decision is based on residual intervals generated by the proposed interval observer. The effectiveness of the proposed approach is highlighted through simulation results of an academic example.
... Safety analysis of flight control systems has a long tradition in the academic control community. Based on linear models which describe the dynamics of the aircraft for individual degrees of freedom, formal methods of control theory such as stability analysis, model-based fault detection and isolation (FDI) and fault tolerant control (FTC) are applied [16], [29]. Due to the nonlinear behaviour of aircraft dynamics, the assumption of an approximate description with linear models is only valid in a small working range. ...
During the climb flight of big passenger airplanes, the airplane’s vertical movement, i.e. its pitch angle, results from the elevator deflection angle chosen by the pilot. If the pitch angle becomes too large, the airplane is in danger of an airflow disruption at the wings, which can cause the airplane to crash. In some airplanes, the pilot is assisted by a software whose task is to prevent airflow disruptions. When the pitch angle becomes greater than a certain threshold, the software overrides the pilot’s decisions with respect to the elevator deflection angle and enforces presumably safe values. While the assistance software can help to prevent human failures, the software itself is also prone to errors and is - generally - a risk to be assessed carefully. For example, if software designers have forgotten that sensors might yield wrong data, the software might cause the pitch angle to become negative. Consequently, the airplane loses height and can - eventually - crash.
In this paper, we provide an executable model written in Matlab/Simulink® for the control system of a passenger airplane. Our model takes also into account the software assisting the pilot to prevent airflow disruptions. When simulating the climb flight using our model, it is easy to see that the airplane might lose height in case the data provided by the pitch angle sensor are wrong. For the opposite case of correct sensor data, the simulation suggests that the control system works correctly and is able to prevent airflow disruptions effectively.
The simulation, however, is not a guarantee for the control system to be safe. For this reason, we translate the Matlab/Simulink® -model into a hybrid program (HP), i.e. into the input syntax of the theorem prover KeYmaera. This paves the way to formally verify safety properties of control systems modelled in Matlab/Simulink®. As an additional contribution of this paper, we discuss the current limitations of our transformation. For example, it turns out that simple proportional (P) controllers can be easily represented by HP programs, but more advanced PD (proportional-derivative) or PID (proportional-integral-derivative) controllers can be represented as HP programs only in exceptional cases.
... Modern FDD approaches usually rely on the model of the system. In this case, if a model mismatch were to exist, the performance of the FDD could degrade significantly [3]. Therefore, increasing the robustness to modeling uncertainty, whilst without losing fault sensitivity is the most crucial point in the model-based FDD concept. ...
This article proposes a nonlinear disturbance observer (NDO) based approach for aircraft inertial measurement unit (IMU) fault detection and diagnosis (FDD) by making use of dynamic and kinematic relations of the aircraft. Furthermore, the detailed aircraft IMU FDD design using four representative fault reconstruction algorithms (NDO, sliding mode observer (SMO), iterated optimal two-stage extended Kalman filter (IOTSEKF) and adaptive two-stage extended Kalman filter (ATSEKF)) is presented. More importantly, this paper presents a thorough FDD performance comparison using these four representative methods. Different FDD performance indexes such as fault detection time, minimum detectable faults and fault estimation errors are compared under various situations such as different fault types and noise standard deviations. The advantages, drawbacks and tuning of each method are investigated, which provide useful insights to aircraft sensor FDD.
... This type of system is used, for example, on NASA's space shuttle orbiter [9]. When such extensive sensing is unavailable, an online fault diagnosis approach for thrusters can play an emergency role in the cases of hardware sensor failure or work as a supplementary reference for online monitoring [10]. ...
Security enhancement and cost reduction have become crucial goals for second-generation reusable launch vehicles (RLV). The thruster is an important actuator for an RLV, and its control normally requires a valve capable of high-frequency operation, which may lead to excessive wear or failure of the thruster valve. This paper aims at developing a thruster fault detection method that can deal with the thruster fault caused by the failure of the thruster valve and play an emergency role in the cases of hardware sensor failure. Firstly, the failure mechanism of the thruster was analyzed and modeled. Then, thruster fault detection was employed by introducing an angular velocity signal, using a blended filter, and determining an isolation threshold. In addition, to support the redundancy management of the thruster, an evaluation method of the nonlinear model-based numerical control prediction was proposed to evaluate whether the remaining fault-free thruster can track the attitude control response performance under the failure of the thruster valve. The simulation results showed that the method is stable and allowed for the effective detection of thruster faults and timely evaluation of recovery performance.
... In addition to, analyzing and assessing risk and reliability in systems for the purpose of improving safety and performance based on some features, such as the spare components, the dependent failures, common cause failure and the failure recoveries. The failure recovery is important issue to monitor and control the health of the satellite since the satellite fulfils their mission in a very challenging environment which is difficulty to eliminate the possibility of the sensor failure and lose the measurements [62][63][64][65][66][67]. ...
Space industry is one of the most important industries in the modern age and used to measure the advancement of countries in the world. Egypt will launch the first satellite is designed and manufactured by Egyptian hands. In this chapter, the proposed unpacking system is developed to introduce monitoring system for the operators in the ground station through three main modules; first, unpacking module, which unpack the received packets of telemetry data from satellite to decode and display this data in readable way to the operators in the ground station. Second, limit checking module for early anomaly detection and third module is developed based on using data mining techniques for predicting the health of battery and estimate remaining useful lifetime. One of the important characteristics of this system is the flexibility of editing that makes it as a generic model compatible with any structure of cube satellite.
Space exploration demands robust spacecraft(SC) subsystems to endure the harsh conditions of space and ensure mission success. Attitude determination and control subsystems (ADCS), as a significant subsystem within SC, are essential for providing the necessary pointing accuracy and stability, and failures in the ADCS can lead to mission failure. Therefore, robust design, thorough testing, and Fault Detection, Isolation and Identification(FDII) techniques are crucial for spacecraft operations. This paper focuses on developing advanced FDII techniques for reaction wheels(RW) within ADCS, evaluating the Prony-based FDII technique for RW, considering its accuracy, time complexity, and memory usage, and Additionally, it introduces new machine learning-based FDII techniques, including enhancements to the Prony-based FDII technique, to manage single faults more effectively. The new proposed techniques used to explore the novel area of multiple faults within the same subsystem. Results indicate that the proposed FDII techniques significantly improve fault detection accuracy, isolation time, and memory efficiency compared to traditional techniques. These advancements enhance the reliability and longevity of spacecraft missions, ensuring that critical subsystems like ADCS operate effectively in the challenging conditions of space. The contributions presented in the paper are introducing three different FDII machine learning-based techniques that support identifying five types of single faults in spacecraft ADCS RW, outperform the Prony-based FDII technique for spacecraft ADCS RW in terms of time and memory complexity, and Finally, improves the fault tolerance of the spacecraft system by detecting Multiple fault combinations that may occur at the same time in one system.
Next-generation aircraft require the development and integration of a deal of innovative green technologies to meet the ambitious sustainability goals set for aviation. Those transformational efforts are associated with a tremendous increase in the complexity of the onboard systems and their multiphysics-coupled behaviors and dynamics. A critical aspect relates to the identification of the coupled faults resulting from the integration of those green technologies, which introduce damage identifiability issues and demand new approaches for the efficient and accurate identification of non-nominal fault conditions. Model-based fault detection and identification (FDI) methodologies have been proven essential to identify onboard the damages affecting the systems from physical signal acquisitions, but existing methods are typically computationally expensive and fail to capture incipient coupled faults, which prevents their adoption and scaling with the increasing complexity of novel multiphysics systems. This work introduces a multifidelity framework to accelerate the identification of fault modes affecting complex systems. An original two-stage compression computes an optimally informative and highly reduced representation of the monitoring signals for the minimum demand of onboard resources. A multifidelity scheme for Bayesian inversion is developed to infer multidomain fault parameters from the compressed signals: variable cost and fidelity models are optimally queried for a major reduction of the overall computational expense. The framework is demonstrated and validated for aerospace electromechanical actuators affected by incipient multimodal faults. Remarkable accelerations of the FDI procedure are observed, and the exact identification of the incipient fault condition was achieved one order of magnitude faster than with standard algorithms.
The impact of disruptive factors originating from the space environment on spacecraft is highly significant in terms of design, operations, and safety. Factors such as solar wind, ionizing radiation, atomic corrosion, particle collisions, extreme vacuum, low gravity, and temperature changes affect spacecraft. Space agencies and research institutes have been working for years to model these effects. In the context of Fault-Tolerant Control (FTC) in spacecraft, numerous preventive measures are taken against disruptive factors of the space environment, aiming for spacecraft to perform their missions reliably for extended periods in space. This study represents the effects of the space environment in Earth's orbit and the precautions taken against these effects during the development and operation of spacecraft systems.
This work develops and simulates the virtual prototype of a conceptual variable modulus airframe for slender microsatellite launch vehicles. To modulate stiffness, piezoelectric transducers are scientifically positioned at the zeros of the derivatives of the slender airframe shape functions. These transducers are actuated by a Proportional Derivative type control law such that the overall system is globally asymptotically stable in the sense of Lyapunov. By considering typical in-flight parameters, the performance of the conceptual synthetic adaptive airframe is studied in comparison with identical airframe that is made of advanced aerospace composites. Simulation results show that for slender beams with aspect ratios; 10, 12 and 15 respectively, the initial amplitudes of vibrations were effectively damped by factors of 85.95%, 3.02% and 6.06% at the instance of piezoelectric reinforcement. At steady states, in the listed order of aspect ratios, the percentage residual amplitudes of the slender beam's oscillations are 1.53%, 48.34% and 46.55%, respectively. In effect, the conceptual adaptive slender airframe demonstrated very good potential for possible utilization on microsatellite launch vehicles. Mp(x,t)-total torque produced by piezoelectric actuators Mpi (x,t)-moment exerted on the beam by the i th actuator Ep-modulus of elasticity of the piezoelectric Eb-beam modulus of elasticity of the piezoelectric d31-Piezoelectric constant lb , wb , tb-length, width and thickness of the beam, respectively. lp , wp , tp-length, width and thickness of the each piezoelectric transducer, respectively. n-total number of identical collocated piezoelectric actuator and sensor pairs that are bonded to the beam eai(t)-electric potential difference of the i th actuator esi(t)-voltage induced in the i th sensor f(x,t)-is the excitation force per unit length acting on the beam in the transverse direction at location x w(x,t)-transverse deflection of the synthetic adaptive airframe wp(x)-transverse deflection of the airframe due to Mp (x, t) wd(x,t)-transverse deflection of the airframe at point x and time t due to () Vxyx-velocity vector for center of gravity of the synthetic adaptive airframe ωxyx-angular velocity vector about the center of gravity of the synthetic adaptive airframe fxyz-total external force vector
The primary focus of this chapter is to design a new interval observer-based Fault Detection (FD) method for a class of discrete-time switched systems subject to unknown but bounded state disturbances and measurement noise. The proposed technique is investigated to reduce the conservatism of gain matrices and to offer more degrees of design freedom by integrating weighted matrices in the structure of the FD observer. Using multiple quadratic Lyapunov functions (MQLF) with an average dwell time (ADT) control condition, novel solvable conditions are derived in terms of linear matrix inequalities (LMIs). Furthermore, the FD decision is based on residual intervals generated by the proposed interval observers. The efficiency of the proposed approach is highlighted through simulation results on an academic example.
Autonomous systems emerge from the need to progressively replace human operators by autonomous agents in a wide variety of application areas. We offer an analysis of the state of art in developing autonomous systems, focusing on design and validation, and showing that the multi-faceted challenges involved go well beyond the limits of weak AI. We argue that traditional model-based techniques are defeated by the complexity of the problem, while solutions based on end-to-end machine learning fail to provide the necessary trustworthiness. We advocate a hybrid design approach, which combines the two, adopting the best of each, and seeks tradeoffs between trustworthiness and performance. We claim that traditional risk analysis and mitigation techniques fail to scale, and discuss the trend of moving away from correctness at design time and towards reliance on runtime assurance techniques. We argue that simulation and testing remain the only realistic approach for global validation, and show how current methods can be adapted to autonomous systems. We conclude by discussing the factors that will play a decisive role in the acceptance of autonomous systems, and by highlighting the urgent need for new theoretical foundations.
Fault detection of non-linear systems is of great importance in control systems reliability. Undetected faults could lead to irreparable damage. This paper deals with fault diagnosis of helicopter system in the presence of uncertainties and disturbances. To deal with sensor, actuator and component faults, the observer-based diagnosis scheme which employs sliding mode observer is designed. Faults are modelled as an additive and multiplicative fault which is introduced as an abrupt and intermittent fault into the system. Observer inequality constraints and gain matrices are solved using a Lyapunov-based approach. The results display the effectiveness of the designed observer and the ability to handle faults.
Purpose
The purpose of this paper, is to solve the problem of finite-time fault-tolerant attitude synchronization and tracking control of multiple rigid bodies in presence of model uncertainty, external disturbances, actuator faults and saturation. It is assumed that the rigid bodies in the formation may encounter loss of effectiveness and/or bias actuator faults.
Design/methodology/approach
For the purpose, adaptive terminal sliding mode control and neural network structure are used, and a new sliding surface is proposed to guarantee known finite-time convergence not only at the reaching phase but also on the sliding surface. The sliding surface is then modified using a proposed auxiliary system to maintain stability under actuator saturation.
Findings
Assuming that the communication topology between the rigid bodies is governed by an undirected connected graph and the upper bounds on the actuators’ faults, estimation error of model uncertainty and external disturbance are unknown, not only the attitudes of the rigid bodies in the formation are synchronized but also they track the time-varying attitude of a virtual leader. Using Lyapunov stability approach, finite-time stability of the proposed control algorithms demonstrated on the sliding phase as well as the reaching phase. The effectiveness of the proposed algorithm is also validated by simulation.
Originality/value
The proposed controller has the advantage that the need for any fault detection and diagnosis mechanism and the upper bounds information on estimation error and external disturbance is eliminated.
This paper deals with fault detection and diagnosis scheme for stochastic non-linear systems using particle filter. To address the problem of fault detection of helicopters in the presence of sensor, actuator, and component faults, the algorithm uses a bank of particle filters running in parallel. The filter monitors the system states and identifies the occurrence of faults. Using the monitored system states, a log-likelihood ratio-based hypothesis testing is performed to detect and isolate faults in the system. Comparing log-likelihood ratio with threshold generated from deviation function of normal model induces the fault decision signal. The algorithm is applied to a 2 Degrees of Freedom helicopter system which is a highly complex, non-linear, and unstable system. The results are presented for sensor, actuator, and component faults represented as additive and multiplicative models. The results show the effectiveness of the algorithm compared with residual generation methods used in fault diagnosis.
This paper considers fault detection problem for a class of discrete-time switched systems with actuator faults. Pole assignment technique and H∞ design are used to develop the fault sensitivity and the disturbance attenuation condition of the residual, respectively. The design conditions of the observer are derived in terms of Linear Matrix Inequalities (LMIs). In addition, the disturbances and measurement noises are supposed to be unknown but bounded by zonotopes. A zonotopic method is then presented to evaluate the residual. A numerical example is performed to illustrate the effectiveness of the proposed method through a comparison with the results obtained without considering sensitivity and robustness analysis.
Fault detection and diagnosis (FDD) for nonlinear systems gains popularity due to its ability to distinguish faults in spite of very high nonlinearity of system dynamics. Increasingly in many application areas, it is important to include parameters of nonlinearity and non-gaussianity to accurately represent the innate dynamics of the physical system. Particle filtering (PF)-based FDD approach is designed to deal with the nonlinearity and non-gaussianity problems of system dynamics. The physical system chosen is a two degrees of freedom helicopter, and PF based approach is developed for various fault cases which includes sensor, actuator, and component faults. Results of various fault cases are presented which shows the identification of fault and time of occurrence of faults in the system.
Due to hovering and VTOL capabilities, in contrast to fixed-wing UAVs, Multi-Rotorcrafts e.g., Quadcopter, Hexacopter etc., are becoming very common for daily life applications, from photography to monitoring and reconnaissance, and from short distance commuting to package delivery. As their applications are increasing, their performance requirements and safety constraints are getting more stringent, which not only require better designs but also better and advanced control strategies with embedded safety constraints such as fault tolerance. In this work, a Robust Nonlinear Dynamic Inversion based control scheme is developed for a class of Multi-Rotorcrafts. Moreover, a \emph{Fault Tolerant Control Allocation} scheme based on modified Redistributed Pseudo Inverse (RPI) and a simple fault detection scheme is developed. It is demonstrated using high fidelity nonlinear simulation and Monte-Carlo simulations that the designed controller provides good tracking in the presence of unknown parametric variations. The controllability of Co-Planar Multi-Rotorcrafts is analyzed, and Available Control Authority Index (ACAI) is extended for non-Hover flight conditions. Attainable Equilibrium Set (AES) as a measure of maneuverability and achievable performance is introduced. Furthermore, for uncontrollable failures, a notion of reduced controllability is introduced, which leads to the safe recovery of Multi-Rotorcraft in the case of uncontrollable rotor failures. Different fault scenarios are investigated in simulation and results are presented. Results demonstrated that the control scheme is capable of accurate trajectory tracking in the presence of controllable failures, and also position tracking and safe recovery in the presence of uncontrollable failures.
The differences of onboard faults characteristics and severity result in different models, methods and interfaces of fault diagnosis. Thus, FDIR (Fault Discovery, Identification and Recovery) systems usually use a hierarchical architecture in centralized or distributed styles. It is difficult for the centralized FDIR to guarantee the timeliness and coverage of fault diagnosis simultaneously, and the distributed one would bring safety problems. Both of them only focus on the health states of spacecrafts, while not considering its own reliability and reusability. Taking advantage of the above two, the architecture proposed by this paper keeps synthetic views of the spacecraft health states at higher levels and distributes local FDIR at lower levels to improve the timeliness and coverage of fault diagnosis simultaneously, which is based on the hierarchical architecture of spacecrafts and fault severity levels. To ensure the safety and reliability of the FDIR system, a highly decoupled runtime model is proposed. To improve the reusability of the architecture, a unified FDIR model is proposed, which includes hierarchical programming interfaces, etc.
This paper discusses the adaptive control allocation based fault tolerant flight control problem for an overactuated aircraft in the presence of unknown uncertainties and actuator faults. Inspired by the feedback of control moments, an innovative adaptive closed-loop control allocation scheme with an estimator for the uncertainties is designed to tackle the distinct non-monotonic and the coupled nonlinearity caused by actuator failures or faults. Furthermore, since actuator faults will cause difficulty in modeling the aircraft dynamics precisely, an adaptive super twisting sliding mode controller is developed to track the reference trajectory. The convergence of the adaptive closed-loop control allocation and the stability of the fault tolerant flight control system is analyzed. Simulation results indicate the effectiveness and performance of the developed controller.
The detection and identification for aircraft icing and actuator/sensor fault has been a lasting topic in flight safety researches. The current algorithms are usually tailored for some specific cases (faults/icing locations, magnitude, etc.). Although the performance of the algorithm in the designated cases may be good, the transferring of it to other different cases is usually heavy as parameters tuning or even algorithm redesigning may be required. In this paper, the author advocates exploring a comprehensive scheme that balance both good performance and wide transferability for different cases. Referring to the current advances in other research communities, we follow the state-of-art Deep Learning (DL) and transfer learning (TL) concepts. A scheme for the icing/actuator fault detection using the DL technique is firstly constructed. The TL is then adopted to transfer this scheme to other different tasks, e.g. fault/icing identification, sensor fault detection. Test results show that the TL-enhanced DL scheme exhibits not only good performance for the designated detection task, but also reflects flexible transferability at low tuning efforts. Via this paper the author advocates furtherly exploring the potentials of the novel DL and TL technique as to advancing the researches/techniques in the flight dynamics and control realm.
As small celestial body exploration advances, higher requirements with regard to system safety and landing precision are proposed for future landing and sample return missions. However, due to limited prior information about the target, the complex dynamics environment, and significant time-delay, performing a descent and landing on the small body surface is challenging. Among all the techniques required for achieving a safe landing, onboard guidance, navigation, and control (GNC) is of paramount importance in determining mission success. In this paper, a systematic survey of the autonomous GNC technologies for descent and landing on small bodies is carried out. First, based on an analysis of the technical challenges in the process, an overview of typical small body landing and sample return missions is given. Then, an elaboration of the state-of-the-art GNC technologies is presented. Specifically, autonomous navigation methods in unknown environments with highly-nonlinear dynamics are introduced. Descent guidance and control algorithms that take into account landing performance optimization and system robustness against model uncertainties are discussed. Touchdown dynamics and control methods proposed for precise and safe surface contact under weak gravity are analyzed. And safe strategies for onboard detected emergencies such as collision threats and system malfunctions are explained. Besides the prevalent methods, innovative techniques with respect to observability-based optimization, edge curve matching, online landing site selection, collision probability-based hazard avoidance, and trajectory curvature guidance proposed for improving system safety and landing performance are elucidated. At last, based on the growing system autonomy and operational complexity demands, a prospect of future research directions for small body GNC technologies is given.
The concept of autonomy is key to the IoT vision promising increasing integration of smart services and systems minimizing human intervention. This vision challenges our capability to build complex open trustworthy autonomous systems. We lack a rigorous common semantic framework for autonomous systems. It is remarkable that the debate about autonomous vehicles focuses almost exclusively on AI and learning techniques while it ignores many other equally important autonomous system design issues. Autonomous systems involve agents and objects coordinated in some common environment so that their collective behavior meets a set of global goals. We propose a general computational model combining a system architecture model and an agent model. The architecture model allows expression of dynamic reconfigurable multi-mode coordination between components. The agent model consists of five interacting modules implementing each one a characteristic function: Perception, Reflection, Goal management, Planning and Self-adaptation. It determines a concept of autonomic complexity accounting for the specific difficulty to build autonomous systems. We emphasize that the main characteristic of autonomous systems is their ability to handle knowledge and adaptively respond to environment changes. We advocate that autonomy should be associated with functionality and not with specific techniques. Machine learning is essential for autonomy although it can meet only a small portion of the needs implied by autonomous system design. We conclude that autonomy is a kind of broad intelligence. Building trustworthy and optimal autonomous systems goes far beyond the AI challenge.
In this paper, a method is presented to design robust fault detection and isolation filters for Linear Parameter Varying (LPV) systems modeled in a Linear Fractional Representation (LFR) fashion. It consists in designing an optimal FDI filter that minimizes the influence of unknown inputs on the residuals, in the Hinfty-norm sense and simultaneously maximizes fault sensitivity performance, in the H- index sense for LPV systems. The design problem is formulated so that all free parameters are optimized via Linear Matrix Inequality techniques. Computational aspects are discussed and it is shown that the proposed solution is structurally well-defined. An illustrative example demonstrates the potential of the proposed method.
A health monitoring system based on analytical redundancy is developed for satellites on elliptical orbits. First, the dynamics of the satellite including orbital mechanics and at-titude dynamics is modelled as a periodic system. Then, periodic fault detection filters are designed to detect and identify the satellite's actuator and sensor faults. In addition, parity equations are constructed using the algebraic redundant relationship among the actuators and sensors. Furthermore, a residual processor is designed to generate the probability of each of the actuator and sensor faults by using a sequential probability test. Finally, the health monitoring system, consisting of periodic fault detection filters, parity equations and residual processor, is evaluated in the simulation in the presence of disturbances and uncertainty.
Fault detection filters are a special class of observers that can generate directional residuals for the purpose of fault isolation. This paper proposes a new approach to design robust (in the disturbance de-coupling sense) fault detection filters which ensure that the residual vector, generated by this filter, has both robust and directional properties. This is done by combining the unknown input observer and fault detection filter principles. The paper proposes a new full-order unknown input observer, and gives necessary and sufficient conditions for its existence. After the disturbance de-coupling conditions are satisfied, the remaining design freedom can be used to make the residual have the directional property, based on the fault detection filter principle. A nonlinear jet engine system is used to illustrate the robust fault isolation approach presented. It is shown that linearization errors can be approximately treated as unknown disturbances and be de-coupled in the design of a robust fault detection filter. Simulation results show that mis-isolation of faults can be avoided using the robust scheme.
Microscope is a satellite due to be launched in March 2008 (Microscope is undertaken jointly by the Centre National d'Etudes Spatiales, France, the ESA, the Office National d'Etude et de Recherche en Aerospatial, France, and the Cote d'Azur Observatory, France). It has the mission of testing the equivalence principle, which postulates the equality between gravitational mass and inertial mass. This paper addresses the fault diagnosis task of the Microscope thrusters. The faulty situations correspond to thrusters blocking themselves and/or closing when in operation. Two model-based diagnosis schemes based on H-infinity/H- filters are proposed and compared with each other in terms of performances and complexity. Nonlinear simulations show that both schemes are able to detect and isolate faults, despite the presence of measurement noises, measurement delays, sensor misalignment phenomena, and disturbances (i.e., third-body disturbances, J(2) disturbances, atmospheric drag, and solar radiation pressure).
This chapter deals with the next step following the design of an FDD system, i.e. appropriate recovery strategies, based on all available actuator/sensor/communication resources. An active fault tolerant flight control strategy based on H
∞
design tools is presented. The Fault Tolerant Control (FTC) strategy operates in such a way that once a fault is detected and confirmed by an FDD unit, a compensation loop is activated for safe recovery. A key feature of the proposed strategy is that the added FTC loop keeps unchanged the in-service control laws facilitating the certification of the whole approach and limiting the underlying Verification and Validation activities. The methodology is applied to actuator fault accommodation of a large commercial aircraft during landing approach. The results, obtained from a piloted 6-DoF flight simulator, will be presented and discussed. The application is taken from the GARTEUR project. The problem studied in this chapter is that of design and analysis of an active flight fault-tolerant control system. The chapter presents a practical case study taken from the European GARTEUR project (Flight Mechanics Action Group 16) on fault-tolerant control. Piloted flight simulator experiments are presented which show that fault tolerance can be achieved provided that there exists sufficient onboard control authority.
This paper discusses the design of a model-based fault detection scheme for robust and early detection of runaways in aircraft control surfaces servo-loop. The proposed scheme can be embedded within the structure of in-service monitoring systems as a part of the Flight Control Computer (FCC) software. The final goal is to contribute to improve the performance detection of unanticipated runaway faulty profiles having very different dynamic behaviors, while retaining a perfect robustness. The paper discusses also the tradeoffs between adequacy of the technique and its implementation level, industrial validation process with Engineering support tools, as well as the tuning aspects. The proposed methodology is based on a combined data-driven and system-based approach using a dedicated Kalman filtering. The technique provides an effective method ensuring robustness and good performance (well-defined real-time characteristics and well-defined error rates). Simulation results, using In-flight recorded data sets provided by Airbus, are presented to demonstrate the potential of the developed technique.
This paper discusses the design of a model-based fault detection scheme for robust and early detection of runaways in aircraft control surfaces servo-loop. The proposed scheme can be embedded within the structure of in-service monitoring systems as a part of the Flight Control Computer (FCC) software. The final goal is to contribute to improve the performance detection of unanticipated runaway faulty profiles having very different dynamic behaviors, while retaining a perfect robustness. The paper discusses also the tradeoffs between adequacy of the technique and its implementation level, industrial validation process with Engineering support tools, as well as the tuning aspects. The proposed methodology is based on a combined data-driven and system-based approach using a dedicated Kalman filtering. The technique provides an effective method ensuring robustness and good performance (well-defined real-time characteristics and well-defined error rates). Simulation results, using In-flight recorded data sets provided by Airbus, are presented to demonstrate the potential of the developed technique.
Fault Detection (FD) plays a vital role in ensuring the safety of a flight-control system, especially that of an uninhabited aerial vehicle. An FD algorithm is designed to detect a situation in which a faulty condition has occurred in the system. The main theoretical contribution of this work is a new residual threshold function, which is input dependent and enhances the FD capabilities of highly uncertain systems. The combined FD algorithm and new threshold function were simulated in the laboratory, in a high-fidelity hardware-in-the-loop environment, and flight tested as part of the Defense Advanced Research Projects Agency (DARPA). Software Enabled Control (SEC) Program. The DARPA SEC program is a research initiative designed to provide flight-control engineers with a reusable interface for the implementation of flight-control algorithms and flight management software on embedded systems.
In May 1999 state-of-the-art autonomy technology was allowed to assume command and control of the Deep Space One spacecraft during the Remote Agent Experiment. This experiment demonstrated numerous autonomy concepts ranging from high-level goal-oriented commanding to on-board planning to robust plan execution to model-based fault protection. Many lessons of value to future enhancements of spacecraft autonomy were learned in preparing for and executing this experiment. This paper describes those lessons and suggests directions of future work in this field.
An integrated design of robust controller and fault estimator for linear parameter varying systems is presented in this paper. Based on gain-scheduled H ∞ design strategy and scaled bounded real lemma, a linear parameter varying controller is developed, which can generate both control signals and fault estimates. To demonstrate the effectiveness of the proposed method, an uncertain system with actuator faults is studied.
This paper presents on line Sensor Fault Detection, Isolation (FDI) and the associated fault tolerant control (FTC) algorithm for a vehicle lateral dynamics represented by the uncertain Takagi-Sugeno (TS) fuzzy model. Fault detection scheme based on a bank of observers based method is considered. Using the LMI formulation, the T-S fuzzy model of the vehicle nonlinear dynamics is used to design an observer based output feedback controller. To demonstrate the effectiveness of the proposed strategy, simulation results are given.
A class of nonlinear systems with faults, parametric uncertainties and without full state measurements are considered. A novel observer is designed whose estimation error is not affected by faults, and an observer-based fault tolerant tracking controller is proposed to make the outputs asymptotically track the reference signals while the states are bounded. The proposed fault tolerant control method can help to provide a switching detection scheme and a family of Lyapunov functions for a class of hybrid nonlinear systems with uncontrollable switchings, and to guarantee the global tracking performance. A three-tank system is taken as an example to show the efficiency of the proposed method.
This paper proposes a new interacting multiple model (IMM) filter for actuator fault detection. Since each individual filter of the IMM filter uses the combined information of the estimation values from all the operating filters, it can effectively estimate system parameter variations, thereby it can diagnose the actuator damage with an unknown magnitude. In this study, to diagnose the actuator failure fast and accurately, fuzzy logic is used to tune a transition probability among multiple models. This makes the fault detection process smooth and reduces the possibility of false fault detection. Also, a discrete fault tolerant command tracker is derived to cope with actuator damages. To validate the performance of the proposed fault detection and diagnosis (FDD) algorithm, numerical simulations are performed for a high performance aircraft system.
This paper deals with the design of robust model-based fault detection and isolation (FDI) systems for atmospheric reentry vehicles. This work draws expertise from actions undertaken within a project at the European level, which develops a collaborative effort between the University of Bordeaux, the European Space Agency, and European Aeronautic Defence and Space Company Astrium on innovative and robust strategies for reusable launch vehicles (RLVs) autonomy. Using an H ∞/ H - setting, a robust residual-based scheme is developed to diagnose faults on the vehicle wing-flap actuators. This design stage is followed by an original and specific diagnosis-oriented analysis phase based on the calculation of the generalized structured singular value. The latter provides a necessary and sufficient condition for robustness and FDI fault sensitivity over the whole vehicle flight trajectory. A key feature of the proposed approach is that the coupling between the in-plane and out-of-plane vehicle motions, as well as the effects that faults could have on the guidance, navigation, and control performances, are explicitly taken into account within the design procedure. The faulty situations are selected by a prior trimmability analysis to determine those for which the remaining healthy control effectors are able to maintain the vehicle around its center of gravity. Finally, some performance indicators including detection time, required onboard computational effort, and CPU time consumption are assessed and discussed. Simulation results are based on a nonlinear benchmark of the HL-20 vehicle under realistic operational conditions during the autolanding phase. The Monte Carlo results are quite encouraging, illustrating clearly the effectiveness of the proposed technique and suggesting that this solution could be considered as a viable candidate for future RLV programs.
Loss of control accounts for over 25% of aircraft accidents worldwide. This study presents a fault tolerant flight control strategy for increasing aircraft safety. The work has been undertaken within the Action Group on fault tolerant control (FTC) of the European GARTEUR programme, which develops collaborative efforts in Europe to create new FTC technologies that could significantly advance the goals of the aviation safety. After the fault detection and confirmation by the in-flight dedicated systems, a compensation loop is activated to ensure safe recovery. The design of the FTC loop is achieved without any changes in the nominal (and certificated) flight control system (FCS). The FTC scheme design is formulated as an H∞ strong stabilisation problem. Fault compensability is subsequently discussed and formulated as a trim-deficiency analysis problem. The proposed technique is implemented on the SIMONA flight simulator and evaluated through a pilot experiment. The tested scenario corresponds to the landing approach of a large transport aircraft (B747-100/200). The faulty situation is related to trimmable horizontal stabiliser (THS) failures. Piloted flight simulator experiments show that fault tolerance can be achieved under the condition that there exists sufficient remaining control authority.
The European Flight Mechanics Action Group FM-AG(16) on Fault Tolerant Control, established in 2004 and concluded in 2008, represented a collaboration involving thirteen European partners from industry, universities and research establishments under the auspices of the Group for Aeronautical Research and Technology in Europe (GARTEUR) program. The book consists of five parts. Part I contains the introduction and motivation of this research project and a state-of-the-art overview in Fault Tolerant Flight Control (FTC). Part II includes the description of the benchmark challenge, consisting of details of the benchmark simulation model and the assessment criteria used to evaluate the performance of the Fault Tolerant Controllers. Part III covers all the different FDI/FTC design methods which have been applied to the benchmark simulation model. There are two different evaluation methods for these FDI/FTC designs, namely an off-line evaluation using the assessment criteria in the benchmark simulation model in Matlab, and an on-line evaluation on Delft’s SIMONA Research Simulator. The off-line evaluations are described in the individual chapters in part III, whereas the latter is treated extensively in part IV where the real time assessments on the SIMONA Research Simulator are introduced and discussed. Finally part V focuses on a review of the applied methods from an industrial perspective together with some concluding remarks.
This paper presents a new and general scheme to design robust fault detection and isolation (FDI) filters for multivariable uncertain systems under feedback control. The design procedure simultaneously ensures robustness of the FDI output against disturbances and modelling errors, and nominal sensitivity to faults. Robust sensitivity of the residual signals is analyzed by means of a test based on the generalized structured singular value. The feedback controller is directly included in the design procedure, making the proposed approach very appealing in most practically relevant situations. Simulation results based on an engine failure scenario of the RCAM benchmark illustrate the potential of the proposed approach.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1973.
In this part of the paper, we review qualitative model representations and search strategies used in fault diagnostic systems. Qualitative models are usually developed based on some fundamental understanding of the physics and chemistry of the process. Various forms of qualitative models such as causal models and abstraction hierarchies are discussed. The relative advantages and disadvantages of these representations are highlighted. In terms of search strategies, we broadly classify them as topographic and symptomatic search techniques. Topographic searches perform malfunction analysis using a template of normal operation, whereas, symptomatic searches look for symptoms to direct the search to the fault location. Various forms of topographic and symptomatic search strategies are discussed.
In this study, adaptive control methodology using multiple models is proposed to design a reconfigurable controller for nonlinear systems. An adaptive model and fixed parameter models are used for mode switching, and a re-initialized adaptive model is considered. The conventional mode switching method based on multiple model adaptive control does not guarantee the stability of the closed-loop system. To improve the adaptiveness of a nonlinear system while maintaining the stability, mode switching scheme is modified and a new decision logic is considered. In the mode switching control scheme, forgetting factor and adaptive time concepts are introduced based on the selected model for reconfiguration. An error threshold value is used in the decision logic. Numerical simulation is performed for a nonlinear discrete-time MIMO aircraft system to verify the effectiveness of the proposed method.
There is an increasing demand for dynamic systems to become more safe and
reliable. This requirement extends beyond the normally accepted
safety-critical systems of nuclear reactors and aircraft where safety is
paramount important, to systems such as autonomous vehicles and fast
railways where the system availability is vital. It is clear that fault
diagnosis (including fault detection and isolation, FDI) has been becoming
an important subject in modern control theory and practice. For example,
the number of papers on FDI presented in many control-related conferences
has been increasing steadily. The subject of fault detection and isolation
continues to mature to an established field of research in control
engineering.
A large amount of knowledge on model-based fault diagnosis has been
accumulated through the literature since the beginning of the 1970s. However,
publications are scattered over many papers and a few edited books. Up to
the end of 1997, there is no any book which presents the subject in an
unified framework. The consequence of this is the lack of ``common
language'', different researchers use different terminology. This problem
has obstructed the progress of model-based FDI techniques and has been
causing great concern in research community. Many survey papers have been
published to tackle this problem. However, a book which presents the
materials in a unified format and provides a comprehensive foundation of
model-based FDI is urgently needed. Such a book would promote the subject
of model-based FDI and make the techniques more accessible for engineers
and research students. This view has been shared by many researchers in
this field. Such a book is also possible, because many important
definitions have been made and the correspondences between different
model-based FDI methods have been established.
This new book presents the subject of model-based FDI in a unified
framework. It contains many important topics and methods, however perfect
coverage and completeness is not the primary concern. The book focuses on
fundamental issues such as basic definitions and the importance of
robustness in FDI approaches. In this book, FDI concepts and methods are
illustrated by either simple academic examples or practical applications.
The first two chapters are of tutorial value and provide a starting point
for new comers to this field. The rest of the book presents the
state-of-the-art in model-based FDI by discussing many important robust
approaches and their applications. This will certainly appeal to experts
in this field.
The book targets both new comers, who want to get into this subject, and
experts, who are concerned with fundamental issues and are also looking
for inspiration for future research. The book is useful for both
researchers in academia and professional engineers in industry because
both theory and applications have been discussed. Although this is a
research monograph, it will be an important text for MSc \& PhD research
students world-wide. The largest market, however will be academics,
libraries and practising engineers and scientists throughout the world.
This study demonstrates that all linear flexible structure do have second-order observers with arbitrary rates of convergence. However, the most natural definition of such an observer does not work. In view of this, an unnatural second-order observer is considered to perform the job.
This paper discusses the effect of measurement errors on the fault detection, fault Isolation and control law reconfiguration algorithms that Honey we 11 has been researching and developing together with NASA Langley Research Center (LaRC) under NASA's Aviation Safety and Security Program. In our previous papers, we developed fault detection, fault isolation, pilot cueing and control law reconfiguration algorithms for a civil transport aircraft, and evaluated the performance of our algorithms in piloted simulation in the Integration Flight Deck facility at NASA LaRC. However, we did not explicitly evaluate the effect of measurement errors (that is, sensor noise, bias, and dynamics) on the performance of our algorithms. In this paper, we add sensor models to LaRC's simulation model and evaluate the performance of our algorithms in the presence of measurement errors. Each algorithm is analyzed separately, and is enhanced by redesigning and/or re-tuned as necessary. The key contribution is that we provide theoretical justification for the architecture of our fault detection algorithm and discuss a systematic procedure for tuning its gains, and we adapt and re-tune our fault isolation algorithm so that it can cope with measurement errors. We also provide results from batch simulations that are representative of the achieved performance in the presence of imperfect measurements.
A most critical and important issue surrounding the design of automatic control systems with the successively increasing complexity is guaranteeing a high system performance over a wide operating range and meeting the requirements on system reliability and dependability. As one of the key technologies for the problem solutions, advanced fault detection and identification (FDI) technology is receiving considerable attention. The objective of this book is to introduce basic model-based FDI schemes, advanced analysis and design algorithms and the needed mathematical and control theory tools at a level for graduate students and researchers as well as for engineers. © 2008 Springer-Verlag Berlin Heidelberg. All rights are reserved.
This paper introduces a systematic design methodology, namely the functional fault analysis (FFA), developed with the goal of integrating SHM into early design of aerospace systems. The basis for the FFA methodology is a high-level, functional model of a system that captures the physical architecture, including the physical connectivity of energy, material, and data flows within the system. The model also contains all sensory information, failure modes associated with each component of the system, the propagation of the effects of these failure modes, and the characteristic timing by which fault effects propagate along the modeled physical paths. Using this integrated model, the designers and system analysts can assess the sensor suitepsilas diagnostic functionality and analyze the ldquoracerdquo between the propagation of fault effects and the fault detection isolation and response (FDIR) mechanisms designed to compensate and respond to them. The Ares I Crew Launch Vehicle has been introduced as a case example to illustrate the use of the Functional Fault Analysis (FFA) methodology during system design.
Fault detection and diagnosis is an important problem in process engineering. It is the central component of abnormal event management (AEM) which has attracted a lot of attention recently. AEM deals with the timely detection, diagnosis and correction of abnormal conditions of faults in a process. Early detection and diagnosis of process faults while the plant is still operating in a controllable region can help avoid abnormal event progression and reduce productivity loss. Since the petrochemical industries lose an estimated 20 billion dollars every year, they have rated AEM as their number one problem that needs to be solved. Hence, there is considerable interest in this field now from industrial practitioners as well as academic researchers, as opposed to a decade or so ago. There is an abundance of literature on process fault diagnosis ranging from analytical methods to artificial intelligence and statistical approaches. From a modelling perspective, there are methods that require accurate process models, semi-quantitative models, or qualitative models. At the other end of the spectrum, there are methods that do not assume any form of model information and rely only on historic process data. In addition, given the process knowledge, there are different search techniques that can be applied to perform diagnosis. Such a collection of bewildering array of methodologies and alternatives often poses a difficult challenge to any aspirant who is not a specialist in these techniques. Some of these ideas seem so far apart from one another that a non-expert researcher or practitioner is often left wondering about the suitability of a method for his or her diagnostic situation. While there have been some excellent reviews in this field in the past, they often focused on a particular branch, such as analytical models, of this broad discipline. The basic aim of this three part series of papers is to provide a systematic and comparative study of various diagnostic methods from different perspectives. We broadly classify fault diagnosis methods into three general categories and review them in three parts. They are quantitative model-based methods, qualitative model-based methods, and process history based methods. In the first part of the series, the problem of fault diagnosis is introduced and approaches based on quantitative models are reviewed. In the remaining two parts, methods based on qualitative models and process history data are reviewed. Furthermore, these disparate methods will be compared and evaluated based on a common set of criteria introduced in the first part of the series. We conclude the series with a discussion on the relationship of fault diagnosis to other process operations and on emerging trends such as hybrid blackboard-based frameworks for fault diagnosis.
Unmanned aerial vehicles (UAVs) offer an incomparable means of gathering intelligence and carrying out missions without needing an onboard human pilot. The benefits are considerable in terms of cost, efficiency, and reduced pilot risk.
In order to complete a mission efficiently and with a high level of safety and security, the following key design points must be met:
• the flight control system must be robust against the aircraft’s model uncertainties and external disturbances;
• an efficient fault detection and isolation (FDI) system should be capable of monitoring the health of the aircraft; and
• the flight control and guidance system should be reconfigurable depending on actuator fault occurrence or aircraft damage, and should be able to avoid obstacles.
Fault-tolerant Flight Control and Guidance Systems addresses all of these aspects with a practical approach following three main requirements: being applicable in real-time; highly computationally efficient; and modular. The text provides:
• an overview of fault-tolerant flight control techniques;
• the necessary equations for the modeling of small UAVs;
• a complete nonlinear FDI system based on extended Kalman filters; and
• a nonlinear flight control and guidance system.
The book is written in a didactic style with many figures and diagrams making it suitable not only for academic researchers and practicing engineers but also graduate students working in the fields of fault detection techniques and the automatic control of UAVs.
This paper deals with industrial practices and strategies for Fault Tolerant Control (FTC) and Fault Detection and Isolation (FDI) in civil aircraft by focusing mainly on a typical Airbus Electrical Flight Control System (EFCS). This system is designed to meet very stringent requirements in terms of safety, availability and reliability that characterized the system dependability. Fault tolerance is designed into the system by the use of stringent processes and rules, which are summarized in the paper. The strategy for monitoring (fault detection) of the system components, as a part of the design for fault tolerance, is also described in this paper. Real application examples and implementation methodology are outlined. Finally, future trends and challenges are presented.This paper is a full version of the invited plenary talk presented by the author on the 1st July 2009 at the 7th IFAC Symposium Safeprocess '09, Barcelona.
This paper presents a new and general scheme to design robust fault detection and isolation (FDI) filters for multivariable uncertain systems under feedback control. The design procedure simultaneously ensures robustness of the FDI output against disturbances and modelling errors, and nominal sensitivity to faults. Robust sensitivity of the residual signals is analyzed by means of a test based on the generalized structured singular value. The feedback controller is directly included in the design procedure, making the proposed approach very appealing in most practically relevant situations. Simulation results based on an engine failure scenario of the RCAM benchmark illustrate the potential of the proposed approach.
The development of robustness analysis tests is presented for quasi-linear-parameter-varying models in the presence of time-varying, parametric uncertainty. The tests are applied for the robustness analysis of nonlinear dynamic inversion control laws giving sufficient conditions for assessing the nonlinear system's performance in the presence of bounded disturbances and uncertainty. The tests are formulated as linear minimisation problems subject to linear matrix inequality constraints and solved by using a gridding procedure. A detailed example is presented in which two nonlinear dynamic inversion control laws for the short-period dynamics of an aircraft are analysed and compared.
In this paper, a reconfigurable control structure called a Fault-Tolerant Rollover Prevention System is proposed. This structure includes active suspensions, active anti-roll bars and an active brake. The purpose of this structure is to improve the performance properties of heavy vehicles, i.e. to reduce the risk of rollovers and to improve passenger comfort and road holding. In the control design, the changes in forward velocity, the performance specifications both for rollover and suspension problems and the model uncertainties are taken into consideration. The design of the control system is based on the H ∞ linear parameter varying (LPV) method. The LPV based control design and the operation of the control mechanism are demonstrated in various vehicle manoeuvres.
An overview is presented of the characteristics of the Airbus fly-by-wire control laws, systems, certification and development methods. First, the general structure of the longitudinal and lateral laws, as well as the architecture of the system transmitting the pilot commands are presented. Next, the design, development and validation procedures used on the A320/A321 programme are described, together with the main certification items. The peculiarities of the A340 control laws and improvements to the design methods experienced during this programme conclude this overview.
A perspective is developed on holy redundancy management techniques for flight-critical systems have matured since the earliest applications in the 1960s, driven largely by the introduction of more powerful computer resources. As this evolution occurred, the basic issues involving system architecture tradeoffs changed very little, although the hardware mechanizations of the earlier analog systems have been replaced largely with the software of the newer digital systems. These basic issues are reviewed and how they tend to be resolved in practical mechanizations is shown. The large body of literature on analytic redundancy theory developed since the 1970s is discussed in the context of its applicability to practical systems. It is shown that analytic redundancy has an important role in real-world systems but that it is not a replacement for physical redundancy, and its proper implementation requires that it be embedded within the physical redundancy structure of the system.
The integration of health management, fault detection and isolation with trajectory reshaping and adaptive guidance and control is a natural and necessary step in producing reliable and responsive autonomous aerospace vehicles. The benefits of reconfigurable control and trajectory reshaping have been demonstrated; however, in many cases these results relied upon the assumption that IVHM/FDI systems provided specific information to the algorithms. Requirements on IVHM/FDI from the perspective of guidance, control and trajectory reshaping have been listed and some opportunities for synergistic information exchange between the two systems have been identified.
In this paper, an application of the robust integrated control/diagnosis approach using ℋ∞-optimization techniques to the nonlinear longitudinal dynamics of a Boeing 747-100/200 aircraft is presented. The integrated approach allows to address directly the trade-off between the conflicting controller and fault diagnosis objectives. The integrated design formulation (interconnection and weight selection) is defined using five LTI plants obtained through out the Up-and-Away flight envelope. Linear and nonlinear closed-loop time simulations are carried out under a realistic turbulence and noise environment. A comparison drawn with the non-integrated design of a controller and a diagnosis filter with the same objectives shows that the integrated case results in similar diagnosis characteristics but improved fault tolerant performance and ease of design. Copyright © 2005 John Wiley & Sons, Ltd.
Two commonly used approaches to sliding mode observer (SMO) design, namely the equivalent control approach of Utkin and the
Walcott and Zak’s observer design strategies are reviewed. Ceratin limitations of each design strategy are discussed and two
alternative design approach for the Walcott and Zak observer based on the representation of a linear system in special coordinate
basis (SCB) from are given. Additionally, a comparative discussion between the SMO and the unknown input observer (UIO) is
provided, along with a discussion on similarities and differences between the two observer design strategies. Next a new sliding
mode observer for linear uncertain systems is discussed. The advantage of this observer is that it can be built under much
less conservative conditions than the one discussed. In addition, we address the issue of estimating a function of the state
as well as unknown inputs or structural uncertainties. Furthermore, basic SMO design idea is extended to certain class of
nonlinear uncertain systems. Next, we discuss how these SMOs can be used for fault detection and isolation (FDI) purposes.
Finally, a number of examples illustrating the application of the SMO in mechatronic applications such as fault diagnosis
of an internal combustion engine, robots, and electric motors are presented.
Fault detection, isolation, and reconfiguration (FDIR) is an important and challenging problem in many engineering applications and continues to be an active area of research in the control community. This paper presents a survey of the various model-based FDIR methods developed in the last decade. In the paper, the FDIR problem is divided into the fault detection and isolation (FDI) step, and the controller reconfiguration step. For FDI, we discuss various model-based techniques to generate residuals that are robust to noise, unknown disturbance, and model uncertainties, as well as various statistical techniques of testing the residuals for abrupt changes (or faults). We then discuss various techniques of implementing reconfigurable control strategy in response to faults.
Multiple model adaptive estimation (MMAE) is applied to the Variable Inflight Stability Test Aircraft (VISTA) F-16 flight control system at a low dynamic pressure flight condition (0.4M at 20000 ft). Single actuator and sensor failures are first, followed by dual actuator and sensor failures. The system is evaluated for complete or "hard" failures, patial or "soft" failures, and combinations of hard and soft actuator and sensor failures. Residual monitoring is discussed for single and dual failure scenarios. Performance is enhanced by the application of a modified Bayesian form of MMAE, scalar residual monitoring to reduce ambiguities, automatic dithering where advantageous, and purposeful commands.
This paper addresses the robust fault estimation problem for a class of linear parameter-varying (LPV) systems with time delays. It is assumed that the state-space data depend affinely on the time-varying parameters that can be measured in real-time and the time-delay is unknown but with bounded variation rates. As an alternative to robust residual generation in FDI, this paper is concerned with the development of a robust fault estimator which has the same parameter dependence as the plant, can be characterized via a set of linear matrix inequalities (LMIs) and has robustness to exogenous disturbance and plant time-delay. To demonstrate the proposed method, an illustrative example is provided.
Robust and early detection of oscillatory failures in the electrical flight control system of an aircraft is a crucial issue. Oscillatory failures, if not detected and passivated in time, can lead to strong interactions with loads and aeroelasticity and may potentially lead to structural damages. A nonlinear observer-based solution to detect such failures with small amplitude at a very early stage is presented. The stability and convergence proofs are given and experimental results with real A380 flight data are presented.
In this paper, a strategy based on the linear quadratic design, which progressively accommodates the feedback control law, is proposed. It significantly reduces the loss of performance that results from the time delay needed by fault accommodation algorithms to provide a solution. An aircraft example is given to illustrate the efficiency of progressive accommodation.
A general approach to fault detection, diagnosis and prognosis in systems describable by mathematical models is outlined. It is based on System Theory and Statistical Decision Theory. The special case of linear dynamic systems with Gaussian random inputs is considered and it is shown how the statistical properties of the innovation process can be used for fault detection and diagnosis.RésuméL'article décrit une approche générale à la détection d'erreurs, au diagnostic et aux prognostics dans des systèmes pouvant être décrits à l'aide de modèles mathématiques. Elle est basée sur la Théorie des Systèmes et la Théorie des Décisions Statisques. L'article considère le cas particulier des systèmes dynamiques linéaires à entrée aléatoires gaussiennes et montre comment les proprietés statistiques du processus d'innovation peuvent être utilisées pour la détection d'erreurs et le diagnostic.ZusammenfassungEin allgemeines Verfahren zur Fehlererkennung, Diagnose und Prognose in Systemen, die durch mathematische Modelle beschreibbar sind, wird skizziert. Es ist auf Systemtheorie und statistische Entscheidungstheorie gegründet. Der Spezialfall linearer dynamischer Systeme mit Gauss'schen Zufallsprozessen als Eingangsgrößen wird betrachtet und es wird gezeigt, wie die statistischen Eigenschaften des neugestalteten Prozesses zur Fehlererkennung und Diagnose benutzt werden können.РефератCтaтья oпиeывaeт oбщий пoдчoд к oбнapyжeнию oшибoк, к диaгнoзy и пpoгнoзy в cиcтe⇐aч oпиcывaeмыч мaтeмaтичecкими мoдeлми. Oн ocнoвaн нa Teopии Cиcтeм и иa Teopии Cтaтиcтичecкич Peшeний. Cтaтья paccмaтpивaeт чacтный cлyчaй линeйныч динaмичecкич cиcтeм c cлyчaйными rayccoвcкими вчoдными кoopдинaтaми и пoкaзывaeт кaк cтaтиcтичecкиe cвoйcтвa пpoцecca oбнoвлeния мoгyт быть иcпoльзoвaны для oбнapyжeня oшибoк и для диaгнoзa.