Jinquan Huang

• Professor at Nanjing University of Aeronautics and Astronautics

A novel degradation estimation method is proposed to draw out the engine performance deterioration rules of multiple rotational-component parameters simultaneous variations from available sensors. The proposed methodology consists of the extended Kalman filtering (EKF) and an enhanced optimization strategy. Gas-path parameter analysis on the impact...

High-precision and real-time modeling are crucial for accelerating the research cycle of next-generation aero-engines. The volumetric dynamics method is acknowledged as the most accurate approach to capture the engine’s transition state process. Nevertheless, the traditional volumetric method encounters challenges, such as neglecting static pressur...

Due to the unique propeller of turboprop engines and the conservative control strategies implemented for safety, traditional turboprop engines are unable to meet current control requirements. Hence, this paper proposes a method for achieving multivariable performance recovery control of turboprop engines using a data-driven predictive model. The me...

The reliability and safety of liquid rocket engine (LRE) are important assessment metrics; the rocket will inevitably deviate from the rated operating conditions in the actual working situation, so in the event of a non-fatal fault, we must make a timely judgment on the trend of the failure's development and take appropriate action. For this reason...

Turboshaft engines are different from each other due to manufacturing and installation tolerances. Hence, it is difficult to draw out the physical model from the average component maps and design points to represent the performance of the individual engine. The available test-bed data is usually less than the number of correction coefficients to up...

This paper addresses sensors’ analytical redundancy by a decision-level data fusion approach to improve the aero-engine control system reliability in the cases of impulsive disturbance and performance degradation. To bridge this gap, we propose a data-driven and model-based decision-level fusion framework to provide analytical redundancies of aero-...

The variable cycle engine switches working modes by way of changing variable-geometry components to achieve the dual advantages of high unit thrust and low specific fuel consumption. Due to the lack of a large amount of rig test data and the complex modeling of rotating components, the incomplete characteristics of the variable-geometry rotating co...

The variable cycle engine selects the appropriate working mode through mode switch to meet the requirements of low specific fuel consumption and high unit thrust. When the variable cycle engine switches between different modes, it should ensure the smooth transition of working modes and obtain the expected performance at the same time. Consequently...

The distributed control system is designed to match the advanced variable cycle engine (VCE) with significant advantages, such as the reduced weight and the low cost by introducing the network. In addition to these advantages, the packet dropout follows and may destroy the performances of the controlled systems. Therefore, except for the engine's s...

This paper proposes a novel intelligent direct thrust control (IDTC) architecture for a two-spool turbofan engine, in which the thrust is directly controlled by the IDTC architecture with two control variables instead of transforming thrust command to other commands. To realize thrust control, the unmeasurable thrust should be estimated first. A dy...

Sensors are the primary information source of the aeroengine control system, their measurement accuracy is closely related to whether the engine can operate safely and efficiently. Aiming at the direct thrust control system of aeroengines, this paper proposes an intelligent prediction algorithm combining feedforward and recurrent networks and a fau...

This paper is concerned with aero-engine components’ health state recognition by a distributed linear Kalman filtering (DKF) in the networked control systems (NCSs), and it involves time delays and data packet drops during parameter transmissions resulting in system uncertainty. Both the measurement filters and fusion center of DKF suffer from syst...

Accurate component maps, which can significantly affect the efficiency, reliability and availability of aero-engines, play a critical role in aero-engine performance simulation. Unfortunately, the information of component maps is insufficient, leading to substantial limitations in practical application, wherein compressors are of particular interes...

A nonlinear model predictive control method based on fuzzy-Sequential Quadratic Programming (SQP) for direct thrust control is proposed in this paper for the sake of improving the accuracy of thrust control. The designed control system includes four parts, namely a predictive model, rolling optimization, online correction, and feedback correction....

A novel estimation-based and dropout-dependent control design for distributed control systems of aeroengines with packet dropout is proposed. The packet dropout is described as an independent identically distributed (i.i.d.) Bernoulli process with known probability. The objective of the control system is to effectively and stably impel aeroengines....

Aero-engines are faced with severe challenges of availability and reliability in the increasing operation, and traditional gas path filtering diagnostic methods have limitations restricted by various factors such as strong nonlinearity of the system and lack of critical sensor information. A method based on the aerothermodynamic inverse model (AIM)...

Deep reinforcement learning has emerged as a powerful control method, especially for the complex nonlinear system such as aeroengine control system, due to its strong representation ability and capability of learning from data measurements. This paper presents a novel control strategy based on deep reinforcement learning to speed up the acceleratio...

A novel turbofan Direct Thrust Control (DTC) architecture based on Linear Parameter-Varying (LPV) approach for a two-spool turbofan engine thrust control is proposed in this paper. Instead of transforming thrust command to shaft speed command and pressure ratio command, the thrust will be directly controlled by an optimal controller with two contro...

A multivariable robust control design for aircraft engines is proposed. The engine is modeled as a fuzzy dynamic system (FDS), which is not the same as Takagi-Sugeno inference system, based on a series of state vector models in the flight envelope. The possible value of uncertainty is prescribed to be within fuzzy sets. The uncertainty is divided i...

Nonlinear control of turbofan engines in the flight envelope has attracted much attention in consideration of the inherent nonlinearity of the engine dynamics. Most nonlinear control design techniques rely on the correction theory of reference model parameter to extend the typical flight operations from ground operation. However, dynamic uncertaint...

There is inevitably a performance deviation between an engine model and an actual engine that is influenced by unpredictable factors such as the unsuspected environmental conditions and the natural performance degradation in the process of use. Because the engine model precision largely depends on the accuracies of the component maps, it is possibl...

Various Kalman filter approaches have been presented for performance estimation of aircraft engines provided that sensor measurement numbers are sufficient and all of them are available. However, it is difficult to collect all physical parameters along the gas path since the complex structure limits sensor installation, especially around the high-p...

This paper addresses the extended Kalman filtering approach to aircraft engine gas path analysis with control loop parameter uncertainty, which results from faulty sensors and actuators. A novel approach of gas path performance anomaly detection is proposed during the life course, which relies on combinations of recovered and actual measurements. A...

A new limit protection method based on Scheduling Command Governor (SCG) is proposed for imposing multiple constraints on a turbofan engine during acceleration process. A Gain Scheduling Controller (GSC) is designed for the transient state control and its stability proof is developed using Linear Matrix Inequalities (LMIs). The SCG is an add-on con...

The aero-engine performance optimization control is to find the appropriate control variables to maximize or minimize the comprehensive index of one or more performance parameters while ensuring its safe operation. Traditional performance seeking control is mainly based on indirect control, with complex structure and large error. This paper develop...

A robust output tracking controller is necessary for the safe and reliable operation of aeroengines. This paper aims at developing an H2/H∞ output tracking approach for aeroengines. In order to improve the tracking performance of the traditional robust tracker, the proposed control structure is designed as a combination of a nominal controller and...

This paper is concerned with a novel state estimator to track gas path performance in real time with one sensor failure and packet dropouts for aircraft engine in an advanced distributed architecture. It is common to sensor measurement lost in the distributed network, which results in the decrease of state tracking accuracy. A hybrid extended Kalma...

Since the operating environment of a turboshaft engine is complex and harsh, gas path components are easily damaged. It is quite important of gas path fault diagnosis for turboshaft engine from the huge accumulated data. The paper presents the data-driven ways of learning machine algorithms for gas path fault diagnosis of the engine and the perform...

Extreme learning machine (ELM) owns the advantages of less computational efforts and simple topology with single-hidden layer structure. However, the performance of plain ELM is sensitive to the input weights, bias, and the number of hidden neurons; and the former two are randomly generated. This paper develops a restricted Boltzmann strategy combi...

The kernel extreme learning machine (KELM) has attracted attention for failure diagnosis of turbofan engines, but its application for time-sensitive scenarios is inherently limited by its lack of sparseness. The original KELM constructs the hidden layer using all the training samples; thus, the real-time performance may be seriously degraded for la...

Providing thrust for the aircrafts is the primary task of the gas turbine engine. Accurate and safe thrust controller design has always been the focus of the research. In this paper, an improved control algorithm for direct performance control of the gas turbine engine is proposed to realize the on-line estimation and tracking of the performance pa...

This paper investigated the problem of fault estimation and fault-tolerant control (FTC) against sensor faults for aircraft engines. By applying a second order sliding mode observer (SOSMO) to the engine on-board model, estimations of the system states and sensor faults could be obtained simultaneously, and the result of state estimation was unaffe...

To accomplish the limit protection task, the Min-Max selection structure is generally adopted in current aircraft engine control strategies. However, since no relationship between controller switching and limit violation is established, this structure is inherently conservative and may produce slower transient responses than the behavior by engine...

Nonlinear component level model (NCLM) is a widely used model for aeroengines. However, it requires iterative calculation and is, therefore, time-consuming, which restricts its real-time application. This study aims at developing a simplified real-time modeling approach for turbofan engines. A mechanism modeling approach is proposed based on linear...

This paper deals with sensor faults of aircraft engines under uncertainties using a bank of second-order sliding mode observers (SMOs). In view of the effect of inevitable uncertainties on the fault reconstruction, a method combining H∞ concepts and linear matrix inequalities (LMIs) is proposed, in which a scaling matrix is designed to minimize the...

This paper proposes a novel architecture of limit protection including the references governors and limit governors and applies this architecture to limit protection in turbofan engines. References governors are designed as add-on schemes to a pre-stability engine control system that modifies reference commands to avoid constraints violation. Limit...

Gas-path performance estimation plays an important role in aero-engine health management, and Kalman Filter (KF) is a well-known technique to estimate performance degradation. In previous studies, it is assumed that different kinds of sensors are with the same sampling rate, and they are used for state estimation by the KF simultaneously. However,...

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original...

Aero-engine gas path health monitoring plays a critical role in Engine Health Management (EHM). To achieve unbiased estimation, traditional filtering methods have strict requirements on measurement parameters which sometimes cannot be measured in engineering. The most typical one is the High-Pressure Turbine (HPT) exit pressure, which is vital to d...

Kernel adaptive filtering (KAF) has gained widespread popularity among the machine learning community for online applications due to its convexity, simplicity and universal approximation ability. However, the network generated by KAF keeps growing with the accumulation of the training samples, which leads to the increasing memory requirement and co...

Kernel adaptive filter (KAF) has been widely utilized for time series prediction due to its online adaptation scheme, universal approximation capability and convexity. Nevertheless, KAF's ability to handle temporal tasks is limited, because it is essentially a feed-forward neural network that lacks dynamic characteristics. Traditionally, a sliding...

This paper is concerned with state estimation approach to track aircraft engine gas-path health condition in an advanced distributed architecture. The sensor measurements are divided into several subsets by installation position along gas path, and they are integrated to estimate engine health state changes with sensor fusion uncertainty. The uncer...

Online sequential extreme learning machine (OS-ELM) learns data one-by-one or chunk-by-chunk, and the recursive least square (RLS) algorithm is commonly employed to train the topological parameters of OS-ELM. Since it is hard to guarantee the smallest estimation error of the state variable by the RLS, the regression performance of the OS-ELM easily...

A new decentralized control for aircraft engines is proposed. In the proposed control approach, aircraft engines are considered as uncertain large-scale systems composed of interconnected uncertain subsystems. For each subsystem, the time-varying uncertainty, including parameter disturbances and interconnections in/between subsystems, is depicted b...

To improve gas-path performance fault pattern recognition for aircraft engines, a new data-driven diagnostic method based on hidden Markov model (HMM) is proposed. A redundant sensor somewhat interferes with fault diagnostic results of the HMM, and it also increases the computational burden. The contribution of this paper is to develop an iterative...

Kalman filter (KF) is a widely used technique to obtain health condition in aero engine health management, and each kind of measurement is commonly assumed to be collected and tackled simultaneously from one sensor in the KF for state tracking in previous studies. However, there are redundant sensor measurements of the same kind employed with diffe...

Performance monitoring is a critical issue for gas turbine engine for improving the operation safety and reducing the maintenance cost. With regard to this, variants of Kalman-filters-based state estimation have been employed to detect gas turbine performance, but the classical centralized Kalman filters are subject to heavy computational effort an...

This paper is concerned with nonlinear Kalman filtering approach to aircraft engine gas path analysis with measurement uncertainty. The uncertain measurements are characterized by time delay and packet dropout. The delay step of physical parameters occurs randomly, and its probability is regulated by a set of uncorrelated variables following Poisso...

Establishing the schemes of accurate and computationally efficient performance estimation and fault diagnosis for turbofan engines has become a new research focus and challenges. It is able to increase reliability and stability of turbofan engine and reduce the life cycle costs. Accurate estimation of turbofan engine performance counts on thoroughl...

The KF (Kalman filter) is the most common state estimation method for gas turbine health monitoring, and it runs in the centralized architecture. However, Health estimation can’t be achieved by the KF-based method as sensor fault occurs, and malfunction of the central monitoring unit will unavoidably result to the termination of the diagnosis task....

A hybrid-scheduling method, consisting of packing algorithm, genetic algorithm and priority promotion algorithm, was proposed for time-triggered CAN in this paper. We divided the basic cycles (BC) into synchronous phase for transmitting time-triggered messages and asynchronous phase for transmitting event-triggered messages. At the each end of BC,...

Kernel adaptive filters (KAFs) generate a linear growing radial basis function (RBF) network with the number of training samples, thereby lacking sparseness. To deal with this drawback, traditional sparsification techniques select a subset of original training data based on a certain criterion to train the network and discard the redundant data dir...

In order to improve the sparsity of kernel-based extreme learning machine (KELM), this paper proposed a novel method named dual reduced kernel extreme learning machine (DR-KELM). The proposed algorithm incorporates traditional greedy forward learning algorithm into backward learning algorithm to gain more sparsity and enhance testing time further....

In order to simultaneously obtain global optimal model structure and coefficients, this paper proposes a novel Wiener model to identify the dynamic and static behavior of a gas turbine engine. An improved kernel extreme learning machine is presented to build up a bank of self-tuning block-oriented Wiener models; the time constant values of linear d...

In this paper the problem of health parameter estimation in an aero-engine is investigated by using an unknown input observer-based methodology, implemented by a second-order sliding mode observer (SOSMO). Unlike the conventional state estimator-based schemes, such as Kalman filters (KF) and sliding mode observers (SMO), the proposed scheme uses a...

In this paper, a H ∞ /Leitmann approach to the robust tracking control design is presented for an uncertain dynamic system. This new method is developed in the following two steps. Firstly, a tracking dynamic system with simultaneous consideration of parameter uncertainty and noise is modeled based on a linear system and a reference model. Accordin...

For a sensor fault diagnostic system of aircraft engines, the health performance degradation is an inevitable interference that cannot be neglected. To address this issue, this paper investigates an integrated on-line sensor fault diagnostic scheme for a commercial aircraft engine based on a sliding mode observer (SMO). In this approach, one slidin...

A novel adaptive weight online sequential extreme learning machine (AWOS-ELM) is proposed for predicting time series problems based on an online sequential extreme learning machine (OS-ELM) in this paper. In real-world online applications, the sequentially coming data chunk usually possesses varying confidence coefficients, and the data chunk with...

A key to achieve reliable model-based engine control, diagnostics and prognostics resides in in-flight engine model with high confidence level. Presented here is a new lifecycle real-time model to describe turbofan engine dynamic behavior called ALPVM (Adaptive Linear Parameter Varying Model), and the issues of engine/model mismatch compensation an...

This paper proposes a novel min-max control scheme for aircraft engines, with the aim of transferring a set of regulated outputs between two set-points, while ensuring a set of auxiliary outputs remain within prescribed constraints. In view of this, an optimal augmented monotonic tracking controller (OAMTC) is proposed, by considering a linear plan...

The on-board sensor fault detection and isolation (FDI) system is essential to guarantee the reliability and safety of an aero engine. In this paper, a novel online sequential extreme learning machine with memory principle (MOS-ELM) is proposed for detecting, isolating, and reconstructing the fault sensor signal of aero engines. In many practical o...

The Kalman filter is widely utilized for gas turbine health monitoring due to its simplicity, robustness, and suitability for real-time implementations. The most common Kalman filter for linear systems is linearized Kalman filter, and for nonlinear systems are extended Kalman filter and unscented Kalman filter. These algorithms have proven their ca...

Gas path fault diagnosis involves the effective utilization of condition-based sensor signals along engine gas path to accurately identify engine performance failure. The rapid development of information processing technology has led to the use of multiple-source information fusion for fault diagnostics. Numerous efforts have been paid to develop d...

Aircraft engine gas-path health monitoring (GPHM) plays a critical role in engine health management (EHM). Among model-based approaches, the Kalman filter (KF) has been widely employed in GPHM. The main shortcoming of KF-based scheme lies in the lack of robustness against uncertainties. To enhance robustness, this paper describes a new GPHM archite...

Various model-based methods are widely used to aircraft engine fault diagnosis, and an accurate engine model is used in these approaches. However, it is difficult to obtain general engine model with high accuracy due to engine individual difference, lifecycle performance deterioration and modeling uncertainty. Recently, data-driven diagnostic appro...

In this article, an improved particle filter with electromagnetism-like mechanism algorithm is proposed for aircraft engine gas-path component abrupt fault diagnosis. In order to avoid the particle degeneracy and sample impoverishment of normal particle filter, the electromagnetism-like mechanism optimization algorithm is introduced into resampling...

A long-term gas-path fault diagnosis and its rapid prototype system are presented for on-line monitoring of a gas turbine engine. Toward this end, a nonlinear hybrid model-based performance estimation and abnormal detection method are proposed in this paper. An adaptive extended Kalman particle filter (AEKPF) estimator is developed and used to real...

To cover the whole range of operating conditions of aero-engine, a double-layer LPV model is built so as to take into account of the variability due to the flight altitude, Mach number and the rotational speed. With this framework, the problem of designing LPV state-feedback robust controller that guarantees desired bounds on both H∞ and H2 perform...

This paper considers the problem of gas turbine transient performance tracking in a cluttered environment. To increase the accuracy and robustness of state estimation, a data-fusion nonlinear estimation method based on an adaptive particle filter (PF) is proposed. This method needs local estimates transmitted to a central filtering unit for data fu...

This paper presents that Inter-stage Turbine Burner (ITB) has a significant potential for increasing specific power of a turbo-shaft engine. The control target is to maximize turbine output power similar to afterburning control scheme for jet engines keeping gas generator working normal. In order to realize the control target, firstly, a component-...

Two system identification approaches are discussed to approximate the nonlinear dynamics of a turbofan engine by constructing linear parameter varying (LPV) models in this paper. The state variables in several steady points from the idle to maximum condition are determined based on the thermodynamic characteristics of engine chambers. The small per...

A closed-loop control law employing compressor guided vanes is firstly investigated to solve unacceptable fuel flow dynamic change in single fuel control for turbo-shaft engine here, especially for rotorcraft in variable rotor speed process. Based on an Augmented Linear Quadratic Regulator (ALQR) algorithm, a dual-input, single-output robust contro...

A hybrid diagnostic method utilizing Extended Kalman Filter (EKF) and Adaptive Genetic Algorithm (AGA) is presented for performance degradation estimation and sensor anomaly detection of turbofan engine. The EKF is used to estimate engine component performance degradation for gas path fault diagnosis. The AGA is introduced in the integrated archite...

For the linear modeling problem of multivariable system of aero-engine, considering the coupling between parameters, a multivariable maximum likelihood (ML) estimation method is researched. An improved expectation-maximization (EM) algorithm integrated genetic algorithm (GA) is proposed and applied to the process of ML identification of frequency d...

The nonlinear model of aero-engine was linearized at multiple operation points by using frequency response method. The validation results indicate high accuracy of static and dynamic characteristics of the linear models. The improved PID tuning method of frequency-domain model matching was proposed with the system stability condition considered. Th...

For many engineering and aerospace power applications, sensor fault diagnosis and recovery on-board is increasing important. Consequently, the development of an efficient and real time diagnostic scheme that can accurately detect and diagnose the sensor failures will offer significant potential for maintaining the safe operating of aircraft engine....

Kalman filters (KF) are widely used in model-based fault diagnosis designs for aircraft engine health management purposes. Nevertheless, health parameter estimation based on KF in the self-tuning on-board real-time model (STORM) cannot always achieve sufficient health monitoring due to the interrelation of different types of sensors and the number...

Aircraft engine sensor fault diagnosis is closely related technology that assists operators in managing the health of gas turbine engine assets. As all gas turbine engines will exhibit performance changes due to usage, the on-board engine model built up initially will no longer track the engine over the course of the engine's life, and then the mod...

The frequency-domain tuning algorithm of multivariable PID controller based on mode matching is applied to aero-engine. The closed-loop stability of the system is ensured by adding the Rosenbrock multivariable closed-loop stability criterion on frequency. The multivariable PID controller was designed for aero-engine and the effectiveness was confir...

Different approaches for gas path performance estimation of dynamic systems are commonly used, the most common being the variants of the Kalman filter. The extended Kalman filter (EKF) method is a popular approach for nonlinear systems which combines the traditional Kalman filtering and linearization techniques to effectively deal with weakly nonli...

Combining a simplified on-board turbo-shaft model with sensor fault diagnostic logic, a model-based sensor fault diagnosis method is proposed. The existing fault diagnosis method for turbo-shaft engine key sensors is mainly based on a double redundancies technique, and this can't be satisfied in some occasions as lack of judgment. The simplified on...

Because of the limited number of sensors, the health estimation results for the gas-path in a turbo-fan engine are uncertain. Based on the contracted Kalman filter, a self tuning on-board model is proposed. By using a matrix transformation, we reduce the dimensions of the health parameter matrix. The weighted sum of the estimation bias and the vari...

In order to acquire the on-board self-tuning model of aero-engine for gas-path analysis, a hybrid method based on the quantum particle swarm optimization (QPSO) is proposed. The method is composed with QPSO and steady state response method, where A and C matrices are obtained by the QPSO to make the state space model outputs fit with nonlinear mode...