International Journal of Control Automation and Systems

Published by Springer Verlag
Online ISSN: 2005-4092
Print ISSN: 1598-6446
Convergence of the current output when no current measurement noise is present.  
Convergence of the current output when current measurement noise is present.  
A method to produce a desired current pattern in a multiple-source EIT system using voltage sources is presented. Application of current patterns to a body is known to be superior to the application of voltage patterns in terms of high spatial frequency noise suppression, resulting in high accuracy in conductivity and permittivity images. Since current sources are difficult and expensive to build, the use of voltage sources to apply the current pattern is desirable. An iterative algorithm presented in this paper generates the necessary voltage pattern that will produce the desired current pattern. The convergence of the algorithm is shown under the condition that the estimation error of the linear mapping matrix from voltage to current is small. Simulation results are presented to illustrate the convergence of the output current.
This paper presents a digital controller for a single phase UPS inverter under two main considerations: (i) the overall system shall keep very low AC-voltage tracking error as well as no phase delay over different load conditions, and (ii) the digital controller shall be employed at a fixed sampling time. It is shown that the first requirement can be achieved by the proposed controller using the error-state approach. Due to the fixed sampling rate, the digital-control design using the emulation method may place a constraint on the speed of time response. We propose that such a problem can be dealt with by the so-called characteristic ratio assignment (Y.C. Kim et al., 2003).
Experimental apparatus.  
Schemetic diagram of self tuning fuzzy PID controller applied to SMA actuator.  
The Structure of PID regulator.  
Shape memory alloy (SMA) actuators, which have ability to return to a predetermined shape when heated, have many potential applications in aeronautics, surgical tools, robotics and so on. Although the number of applications is increasing, there has been limited success in precise motion control since the systems are disturbed by unknown factors beside their inherent nonlinear hysteresis or the surrounding environment of the systems is changed. This paper presents a new development of SMA position control system by using self-tuning fuzzy PID controller. The use of this control algorithm is to tune the parameters of the PID controller by integrating fuzzy inference and producing a fuzzy adaptive PID controller that can be used to improve the control performance of nonlinear systems. The experimental results of position control of SMA actuators using conventional and self tuning fuzzy PID controller are both included in this paper
The purpose of this paper is to marry the two concepts of multiple model adaptive control and safe adaptive control. In its simplest form, multiple model adaptive control involves a supervisory switching among one of a finite number of controllers as more is learnt about the plant, until one of the controllers is finally selected and remains unchanged. Safe adaptive control is concerned with ensuring that when the controller is changed the closed-loop is never unstable. This paper introduces a receding horizon multiple model, switching and tuning control scheme based on an on-line redesign of the controller. This control scheme has a natural two-stage adaptive control algorithm: identification of the closest model and design of the control law. The computational complexity aspects of this approach to adaptive control are discussed briefly. A nonlinear system is used to illustrate the ideas
In the adaptive fuzzy and neural network control field, there are two basic configurations: direct and indirect. It is well known that the direct configuration needs more restrictions on the control gain than the indirect configuration. In this paper, we propose a direct adaptive fuzzy controller with less restrictions on the control gain. Using an extension of the universal approximation theorem, we show that the only required constraint on the control gain is that its sign is known. We also show that using the approximation error estimator enhances performance. Finally, application to an inverted pendulum demonstrates the effectiveness of the proposed controller.
Camera projection diagram showing the reference frame ( F * ), the current frame (F) and the desired frame ( d F ).  
Evolution of the position of the mobile throughout a stabilization mission.
Divergence of the two estimations: ˆ ρ (dashed line) andˆbandˆ andˆb (solid line) during a stabilization mission.
This paper describes a visual tracking control law of an Unmanned Aerial Vehicle (UAV) for monitoring of structures and maintenance of bridges. It presents a control law based on computer vision for quasi-stationary flights above a planar target. The first part of the UAV’s mission is the navigation from an initial position to a final position to define a desired trajectory in an unknown 3D environment. The proposed method uses the homography matrix computed from the visual information and derives, using backstepping techniques, an adaptive nonlinear tracking control law allowing the effective tracking and depth estimation. The depth represents the desired distance separating the camera from the target.
The temperature control system has nonlinear time-varying, slow response, time-delay and un-symmetric control input dynamic characteristics. It is difficult to accurately estimate the dynamic model and design a general purpose temperature controller for achieving good control performance. Here a model-free intelligent fuzzy sliding mode control strategy is employed to design a temperature controller with gain-scheduling scheme or auto gain-tuning algorithm for a heating input only closed chamber. The concept of gain scheduling is employed to adjust the mapping ranges of the input and output variables of fuzzy membership functions during the control process for improving the transient and steady-state control performances. The experimental results show that the steady state error of the step input response is always less than 0.2 C without overshoot by using this control schemes.
A dynamic file grouping strategy is presented to address the load balancing problem in streaming media clustered server systems. This strategy increases the server cluster availability by balancing the workloads among the servers within a cluster. Additionally, it improves the access hit ratio of cached files in delivery servers to alleviate the limitation of I/O bandwidth of storage node. First, the load balancing problem is formulated as a two layer semi-Markov switching state-space control process. Then, a gradient-based reinforcement learning algorithm is proposed to optimize the grouping policy online. This analytic model captures the behaviors of streaming media clustered server systems accurately, and is with constructional flexibility and scalability. By utilizing the features of the event-driven policy, the proposed optimization algorithm is adaptive and with less computational cost. Simulation results demonstrate the effectiveness of the proposed approach.
This paper extends the worst-case norm (WCN) of linear systems subject to inputs with magnitude and rate bounds to the case of uncertain linear systems. While the WCN for linear systems can be accurately obtained by simply solving a sparse linear programming, the computation of the WCN for uncertain linear systems leads to an NP-hard problem. In this paper, a branch-and-bound algorithm is adopted to calculate the WCN in the presence of uncertainty. Numerical examples demonstrate that computation time of the proposed algorithm is reasonable within certain problem dimensions. An exhaustive search is employed to validate the branch-and-bound algorithm, which later indicates the positive outcome. Finally, we suggest a means to improve the WCN computation for problems with higher dimensions
In this paper, moving a fragile object from an initial point to a specific location in the minimum time without damage is studied. In order to achieve this goal, initially, the range of maximum acceleration and velocity are specified, which the manipulator can generate dynamically on the path that is planned a priori considering the geometrical constraints. Later, considering the impulsive force constraint on the object, the range of maximum acceleration and velocity are obtained to keep the object safe while the manipulator is carrying it along the curved path. Finally, a time-optimal trajectory is planned within the maximum allowable range of acceleration and velocity. This time-optimal trajectory planning can be applied to real applications and is suitable for not only a continuous path but also a discrete path.
Responses of δ(t) at λ=0.4 (plot 1) and λ=0.39 (plot 2) with excitation controller only.
Response of P e (t) at λ=0.01 with the proposed excitation and TCPS controller. 
This paper presents a new approach to thyristor controlled phase shifter (TCPS) control. We propose a nonlinear coordinated generator excitation and TCPS controller to enhance the transient stability of a power system. The proposed controller is able to control the three main parameters affecting AC power transmission: namely the excitation voltage, phase angle and reactance in a coordinated manner. The TCPS is located at the midpoint of the transmission line. A nonlinear feedback control law is proposed to linearize and decouple the power system. The design of the proposed controller is based on the local measurements only. Digital simulation results are shown to demonstrate the effectiveness of the proposed controller for the enhancement of transient stability of the power system under a large sudden fault
An intelligent optimization method for designing fractional order PID (FOPID) controllers based on particle swarm optimization (PSO) is presented in this paper. Fractional calculus can provide novel and higher performance extension for FOPID controllers. However, the difficulties of designing FOPID controllers increase, because FOPID controllers append derivative order and integral order in comparison with traditional PID controllers. To design the parameters of FOPID controllers, the enhanced PSO algorithm is adopted, which guarantee the particle position inside the defined search spaces. The optimization performance target is the weighted combination of ITAE and control input. The numerical realization of FOPID controllers uses the methods of Tustin operator and continued fraction expansion. Experimental results show the proposed method is highly effective
Terminal voltage responses when there is no excitation control.
This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate the effectiveness of the proposed controller design.
In this paper, we consider stability analysis and design for switched systems consisting of linear descriptor systems that have the same descriptor matrix. When all descriptor systems are stable, we show that if the descriptor matrix and all the subsystem matrices are commutative pairwise, then the switched system is stable under arbitrary switching. This is an extension of the existing well known result in (Narendra and Balakrishnan, 1994) for switched linear systems with state space models to switched descriptor systems. Under the same commutation condition, we also show that in the case where all the descriptor systems are not stable, if there is a stable convex combination of the unstable descriptor systems, then we can establish a class of switching laws which stabilize the switched system
In this paper, we consider stability analysis and design for switched systems consisting of linear discrete-time descriptor subsystems. When all descriptor subsystems are stable, we show that if the descriptor matrix and all the subsystem matrices are commutative pairwise, then the switched system is stable under arbitrary switching. We also extend the result to the case where all subsystems have different descriptor matrices. Under the same commutation condition, we show that in the case where all the descriptor subsystems are not stable, if there is a stable combination of the unstable descriptor subsystems, then we can establish a class of switching laws which stabilize the switched descriptor system.
We consider the problem of tracking the output of an unmanned tandem helicopter. We investigate the dynamic model and analyze the exact linearization. We present the approximate linearization to design the controller for output tracking based on dynamic extension method. Simulation results show the effectiveness of the method.
This article presents the implementation of position control of a mobile inverted pendulum(MIP) system by using the radial basis function network(RBF). The MIP has two wheels to move on the plane and to balance the pendulum. The MIP is known as a nonlinear system whose dynamics is non-holonomic. The goal is to control the MIP to maintain the balance of the pendulum while tracking a desired position of the cart. The reference compensation technique (RCT) scheme is used as a neural network control method to control the MIP. The back propagation learning algorithm for the RBF network is derived for on-line learning and control. The control algorithm has been embedded on a DSP 2812 board to achieve real-time control. Experimental results are conducted and show successful control performances of both balancing and tracking the position of the MIP.
Part-I of this two-part paper develops an optimal algorithm for transient stability control to coordinate the preventive actions and emergency actions for a subset of contingencies with an identical unstable mode. In this portion, several subsets of contingencies having dissimilar unstable modes are dealt with. Preventive actions benefiting a subset of contingencies may go against the stability of others, thus coordination among the optimal schemes for individual subsets is necessary. The coordination can be achieved by replacing some preventive actions with contingency-specified emergency actions. It is formulated as a classical model of economic dispatch with stability constraints and stability control costs. Such an optimal algorithm is proposed based on the algorithm in Part-I of the paper and is verified by simulations on a Chinese power system.
A computer vision technique to identify the location of an outdoor unmanned ground vehicle (UGV) is presented. The proposed technique is based on hybrid 3D registration of 360 degree laser range data to a digital surface model (DSM). Range frames obtained from 48 laser detectors are aligned with the reference coordinate system of the DSM. Three novel approaches are proposed for accurate and fast 3D registration of range data and the DSM. First, a two-step hybrid 3D registration technique is proposed. A pair-wise registration step of two consecutive range frames is followed by a refinement step using a layered DSM. Second, a fast projection-based pair-wise registration is proposed by employing rasterized 360 degree range frames. Third, a high elevation DSM is divided into several elevation layers and correspondence search is done near the vehicle’s current elevation. This reduces the number of matching outliers and facilitates fast localization. Experimental results show that the proposed approaches yield better performance in 3D localization compared to conventional 3D registration techniques. Error analysis on five outdoor paths is presented with respect to ground truth. Keywords3D registration–DSM–LIDAR–Localization–UGV
This paper presents two types of nonlinear controllers for an autonomous quadrotor helicopter. One type, a feedback linearization controller involves high-order derivative terms and turns out to be quite sensitive to sensor noise as well as modeling uncertainty. The second type involves a new approach to an adaptive sliding mode controller using input augmentation in order to account for the underactuated property of the helicopter, sensor noise, and uncertainty without using control inputs of large magnitude. The sliding mode controller performs very well under noisy conditions, and adaptation can effectively estimate uncertainty such as ground effects. KeywordsFeedback linearization-sliding mode control-UAV-quadrotor helicopter
In this study, we developed a small 3 times zoom lens barrel for a 5M camera module to load a mobile phone set. Its dimensions are 16mm × 9mm × 28mm and is constructed with 8 sheets of lenses, two step motors, some gears, a cam plate and so on.
This work presents a trajectory control for non-redundant serial-link manipulators that is valid for trajectories with ordinary singularities of codimension one and non-ordinary singularities of any codimension. For this purpose, several singularity classifications are considered and a procedure is developed in order to solve the indeterminate motion of non-ordinary singularities. The proposed trajectory control is validated by simulation and by experiments with the six-revolute (6R) industrial robot KUKA KR 15/2.
Projecting the spatio-temporal clip onto the XOY, XOT and YOT planes.
The result of extending the area from the two- dimensional Gaussian distribution to the whole spatio-temporal clip. 
The process of getting the size-adapted spatio- temporal cuboid. 
Abnormal crowd behavior detection is an important research issue in computer vision. However, complex real-life situations (e.g., severe occlusion, over-crowding, etc.) still challenge the effectiveness of previous algorithms. Recently, the methods based on spatio-temporal cuboid are popular in video analysis. To our knowledge, the spatio-temporal cuboid is always extracted randomly from a video sequence in the existing methods. The size of each cuboid and the total number of cuboids are determined empirically. The extracted features either contain the redundant information or lose a lot of important information which extremely affect the accuracy. In this paper, we propose an improved method. In our method, the spatio-temporal cuboid is no longer determined arbitrarily, but by the information contained in the video sequence. The spatio-temporal cuboid is extracted from video sequence with adaptive size. The total number of cuboids and the extracting positions can be determined automatically. Moreover, to compute the similarity between two spatio-temporal cuboids with different sizes, we design a novel data structure of codebook which is constructed as a set of two-level trees. The experiment results show that the detection rates of false positive and false negative are significantly reduced. Keywords: Codebook, latent dirichlet allocation (LDA), social force model, spatio-temporal cuboid.
This paper provides a brief presentation and a useful comparison between two nonlinear observers Extended Kalman Filter (EKF) and sliding mode observer (SMO). Both can be used for moderate-accuracy attitude determination systems for Low Earth Orbit (LEO) Earth-pointing spacecraft (s/c), which is typically achieved using Gyroscopes, Earth, and Sun sensors for attitude sensing. The use of these observers provides a substitute for the yaw data in case of the s/c eclipse periods or limited field of views. The nonlinear observability for this system is analytically investigated via the calculation of Lie derivatives to check the possibility of the system states estimation. The performances of both observers are presented, the SMO stability is proved and the SMO enhanced estimates are shown by simulation. KeywordsEKF-nonlinear observability-sliding mode observer-spacecraft attitude-stability
The problem of absolute stability of Lur’e systems with time-delay is investigated in this paper. By employing a new Lyapunov-Krasovskii functional with the idea of N-segmentation of delay length, less conservative delay-dependent stability criteria are obtained and formulated in the form of linear matrix inequalities (LMIs). Numerical example shows that the results obtained in this paper are better than existing ones.
(a) Induction motor (IM) drive system (b) PI predictive controller.
Starting performances at no load.
In this paper a new robust adaptive speed controller algorithm for AC motor drives is presented. The main feature of this algorithm is that minimum synthesis is required to implement the strategy. MCS algorithm is a significant development of MRAC. The stability of the proposed system is achieved through Popov’s Hyperstability criteria. The new algorithm appeared to be robust in the face of totally unknown plant dynamics, external disturbances and parameter variations with the plant. Finally, a new approach has been successfully implemented on DTC-SVM. Extensive simulation results are presented to validate the proposed technique. The system is tested at different speeds and a very satisfactory performance has been achieved. KeywordsDTC-SVM–minimum controller synthesis (MCS)–parameter variation–robust approach
In this paper, we present the analysis of grasp stability for multi-fingered robot hands that is based on translational and rotational acceleration convex polytopes. The aim of the grasp stability analysis is to find the resistance forces and moments of robot hands that can withstand the external disturbance forces and moments applied on objects. We calculate the resistance forces and moments respectively which are considered the properties of objects and robots. Therefore, the resistance forces and moments depend on the joint driving torque limits, the posture and the mass of robot fingers, the configuration and the mass of objects, the grasp position, the friction coefficients between the object surface and the end-effectors of robot fingers. We produce the critical resistance force and moment which are absolutely stable about external disturbances in all directions, the global resistance force and moment which are whole grasp capability of robot hands, and the weighted resistance forces and moments which can be properly used by controlling two indices according to the importance of robot hands. The effectiveness of this method is verified with simulation examples.
In this paper, it is presented a novel approach for the self-sustained resonant accelerometer design, which takes advantages of an automatic gain control in achieving stabilized oscillation dynamics. Through the proposed system modeling and loop transformation, the feedback controller is designed to maintain uniform oscillation amplitude under dynamic input accelerations. The fabrication process for the mechanical structure is illustrated in brief. Computer simulation and experimental results show the feasibility of the proposed accelerometer design, which is applicable to a control grade inertial sense system.
This paper considers the problem of fault estimation and accommodation for a class of switched systems with time-varying delay. An adaptive fault estimation algorithm is proposed to estimate the fault, moreover, constant or time-varying fault can be estimated. Meanwhile, a delay-dependent criteria is obtained with the purpose of reducing the conservatism of the fault estimation algorithm design. On the basis of fault estimation, an observer-based fault tolerant controller is designed to guarantee the stability of the closed-loop system. Additionally, simulation results are presented to illustrate the efficiency of the proposed results. KeywordsAccommodation–fault estimation–switched systems–time-varying delay
This paper proposes a novel processor for genetic algorithm (GA) that can dynamically change number of individuals and accuracy. In conventional GA, number of population and accuracy are fixed. However, the accuracy of solution is low at first-half stage. Therefore, the number of population is doubled at expense of the accuracy of solution, and the searching ability is improved at first-stage in the proposed GA processor. Then, the number of population is reduced by half, and the accuracy is improved at second-half stage. As a result, the searching ability is improved. The proposed GA processor was designed and verified. The effectiveness of proposed method was confirmed by applying to the knapsack problem.
This paper proposes the design of anti-windup compensator gain for improving stability of actuator input constrained linear multiple state delays systems. The system state delays are classified into mixed delay-dependent/delay-independent analysis and described by delay-differential equations. The real scalar delays are assumed to be fixed and unknown, but with known coefficient matrices. It is shown that the closed-loop system containing the controller plus the anti-windup gain can be modeled as a linear system with dead-zone nonlinearity. The formulation of anti-windup compensator gain is based on convex optimization using linear matrix inequalities (LMI) that ensure closed-loop asymptotic stability of the system while accounting upper-bound delays. The devised LMIs based on Lyapunov-Krasovskii functionals prove significantly less conservative in giving higher upper bounds delays in the formulation of anti-windup gain besides ensuring closed-loop asymptotic stability. KeywordsAnti-windup compensator–linear matrix inequalities–system identification–time-delay systems
The increasing demand for high-speed performance and low energy consumption has necessitated the design of lightweight mechanical systems. The active vibration suppression of a flexible manipulator is important in many engineering applications, such as robot manipulators and high-speed flexible mechanisms, because the flexibility of lightweight manipulators induces a vibration problem. Frequently, the optimal parameters determined for a certain control algorithm might not cover a wide range of operating conditions. Hence, we have proposed and developed a lookup table control method for a flexible manipulator that can tune itself to optimal parameters on the basis of the initial maximum responses of the controlled system and a genetic algorithm. The genetic algorithm is used to search for optimal parameters with regard to positive position feedback and thereby minimizes the objective functions determined from the initial maximum responses. Our lookup table, which has the optimal parameters of the positive position feedback as a function of the initial maximum responses, can be used in a real-time control algorithm.
In this paper, direct adaptive-state feedback control schemes are developed to solve the robust tracking and model matching control problem for a class of distributed large scale systems with actuator faults, faulty and perturbed interconnection links, and external disturbances. The adaptation laws are proposed to update the controller parameters on-line when all the eventual faults, the upper bounds of perturbations and disturbances are assumed to be unknown. Then a class of distributed state feedback controllers is constructed to automatically compensate the fault, perturbation and disturbance effects based on the information from adaptive schemes. The proposed distributed adaptive tracking controller can ensure that the resulting adaptive closed-loop large-scale system is stable and the tracking error decreases asymptotically to zero in the presence of uncertain faults of actuators and interconnections, perturbations in interconnection channels, and disturbances. The proposed adaptive design technique is finally evaluated in the light of a simulation example.
A radial basis function neural network sliding-mode controller (RBFSMC) is proposed to control a shape memory alloy (SMA) actuator. This approach, which combines a RBF neural network with sliding-mode control (SMC), is presented for the tracking control of a class of nonlinear systems having parameter uncertainties. The centers and output weights of the RBF neural network are updated through on-line learning, which causes the output of the neural network control to approximate the sliding-mode equivalent control along the direction that makes the sliding-mode asymptotically stable. Using Lyapunov theory, the asymptotic stability of the overall system is proven. Then, the controller is applied to compensate for the hysteresis phenomenon seen in SMA. The results show that the controller was applied successfully. The control results are also compared to those of a conventional SMC. KeywordsAdaptive control–RBF neural network–shape memory alloy control–sliding mode control
In this study, a simulation model for a powered hip orthosis (PHO) with air muscles to predict the gait of paraplegics is presented which can be used as a design tool for hip orthoses. Before simulation, mathematical models for a human dummy with an orthosis and a pneumatic muscle actuator were generated. For the air muscle, coefficients required were obtained by static and dynamic experiments of the air muscle and experiments for the valve controlling the air pressure. The computation was conducted on the ADAMS package together with MATLAB. Computer simulation of the flexion of hip joints by the pneumatic muscle results in similar values to those from gait analysis. With the development of a simulation model for a PHO, the gait simulation model using pneumatic muscles can be used to analyze and evaluate the characteristics and efficiency of a PHO by setting the input and boundary conditions. KeywordsAir muscle-paraplegic-pneumatic actuators-powered hip orthosis-simulation
In this paper, we have introduced a prototype of a fish robot driven by unimorph piezoceramic actuators. To improve the swimming performance of the fish robot in terms of tail-beat angle, swimming speed, and thrust force, we used four light-weight piezo-composite actuators (LIPCAs) instead of the two LIPCAs used in the previous model. We also developed a new actuation mechanism consisting of links and gears. Performance tests of the fish robot were conducted in water at various tail-beat frequencies to measure the tail-beat angle, swimming speed, and thrust force. The tail-beat angle was significantly better than that of the previous model. The best tail-beat frequency of the fish robot was 1.4 Hz and the maximum thrust force was 0.0048 N. A miniaturized power supply, which was developed to excite the LIPCAs, was installed inside the fish robot body for free swimming. The maximum free-swimming speed was 3.2 cm/s.
An implementable and practical steering law of control moment gyros (CMGs) to avoid singularity is addressed in this paper. The singularity strategy of CMGs revised in this paper is based on a simple but practical virtual actuator methodology. It is known that in a special case of singularity and torque requirement, the virtual CMGs can provide perfect command torque without torque error, which can be proven analytically. In this paper, much extensive analysis is accomplished to provide the performance of the virtual actuator concept, which can avoid the singular configuration of CMGs with possibly smaller control torque error than the conventional singularity robustness method. Finally, the steering law based on the virtual actuator concept is demonstrated by numerical simulations. KeywordsControl moment gyros-singularity avoidance-virtual actuators
This paper studies the control of nonlinear Galerkin systems, which are an important class of nonlinear systems that arise in reduced-order modeling of infinite-dimensional systems. A novel approach is proposed in which a linear parameter-varying (LPV) model representing the Galerkin model is built, where the parameter variation is dictated by a specially designed adaptation scheme. The controller design is then carried out on the simpler LPV model, instead of dealing directly with the complicated nonlinear Galerkin system. An automatically scheduled H-infinity controller is designed using the LPV model, and it is proven that this controller will indeed achieve the desired stabilization when applied to the nonlinear Galerkin model. The approach is illustrated with an example on cavity flow control, where the design is seen to produce satisfactory results in suppressing unwanted oscillations. KeywordsAdaptation-cavity flow-flow control-Galerkin systems-H-infinity control-linear parameter varying (LPV) systems-self scheduling
Parameter values of the quarter-car used in this paper. 
Damping force characteristics of a typical MR damper.
In this paper, a road-frequency adaptive control for semi-active suspension systems is investigated. The control aims to improve the vehicle suspension performance (ride comfort and wheel handling) for all frequency regions of road disturbances. In order to achieve this aim, the control law is extended from the conventional skyhook control, and the controller gains are scheduled for various frequency regions of road disturbances. By using the data measured from a relative displacement sensor, a state estimator based on a Kalman filter for estimating the required state variables is designed. Road disturbance frequencies are estimated by using a first order zero-crossing algorithm. The efficiency of the proposed control is shown through numerical simulations. KeywordsCar suspension control-minimum norm-relative displacement sensor-road-frequency adaptive
In this paper, a stable adaptive neural sliding mode controller is developed for a class of multivariable uncertain nonlinear systems. For these systems not all state variables are available for measurements. By designing a state observer, adaptive neural systems, which are used to model unknown functions, can be constructed using the state estimations. Based on Lyapunov stability theorem, the proposed adaptive neural control system can guarantee the stability of the whole closed loop system and obtain good tracking performances. Adaptive laws are proposed to adjust the free parameters of the neural models. Simulation results illustrate the design procedure and demonstrate the tracking performances of the proposed controller. KeywordsAdaptive neural control-MIMO nonlinear systems-observer-robustness-sliding mode control-stability
Traditional filtering theory is always based on optimization of the expected value of a suitably chosen function of error, such as the minimum mean-square error (MMSE) criterion, the minimum error entropy (MEE) criterion, and so on. None of those criteria could capture all the probabilistic information about the error distribution. In this work, we propose a novel approach to shape the probability density function (PDF) of the errors in adaptive filtering. As the PDF contains all the probabilistic information, the proposed approach can be used to obtain the desired variance or entropy, and is expected to be useful in the complex signal processing and learning systems. In our method, the information divergence between the actual errors and the desired errors is chosen as the cost function, which is estimated by kernel approach. Some important properties of the estimated divergence are presented. Also, for the finite impulse response (FIR) filter, a stochastic gradient algorithm is derived. Finally, simulation examples illustrate the effectiveness of this algorithm in adaptive system training.
This article presents an indirect adaptive fuzzy control scheme for a class of nonlinear uncertain nonaffine systems with unknown control directions. The nonlinear nonaffine system is first transformed into an affine form by using a Taylor series expansion, and then fuzzy systems are employed to approximate the equivalent affine system’s unknown nonlinearities. By modifying the estimated input control gain and using a novel smooth robust control term, a stable well-defined adaptive controller is proposed. Simulation results are provided to illustrate the efficiency of the proposed scheme. KeywordsAdaptive control-fuzzy control-nonaffine systems-unknown control direction
This paper represents an application of a neural network-based adaptive control to the Stability and Control Augmentation System(SCAS) of an unmanned airship whose maneuvers consist of diverse flight phases at low speeds. The neural network (NN) based adaptive SCAS is based on the inversion of a linear model of the airship at a nominal operating point and the adaptation of neural networks to unmodeled dynamics, parameter variations, and uncertain environments. This paper also presents an evaluation of the adaptive SCAS with flight test results and simulation results. In this evaluation, an outer-loop control is used. The autopilot is designed using a classical PID control algorithm for trajectory line tracking and altitude hold modes. Moreover, the adaptive SCAS approach showed superiority over the classical PID design approach in terms of the gain tuning process during a flight test.
Evolution of ) ( and ) ( ), ( 1 1 1 k x k x k x m
Membership functions.  
Evolution of x 1 , 1 ˆ x estimate and x m1 reference in presence of parameter uncertainties.  
Evolution of x 2 , 2 ˆ x estimate and x m2 reference in presence of parameter uncertainties.  
Evolution of the error position in presence of parameter uncertainties.  
In this paper we are interested in robust adaptive fuzzy control of nonlinear SISO systems in the presence of parametric uncertainties. The plant model structure is represented by the Takagi-Sugeno (T-S) type fuzzy system. An indirect adaptive fuzzy controller based on model reference control scheme is proposed to provide asymptotic tracking of reference signal. The controller parameters are computed at each time. The plant state tracks asymptotically the state of the reference model for any bounded reference input signal. Inverted pendulum and mass spring damper are used to check the performance of the proposed controller.
This paper presents a methodological approach to design observer-based adaptive sliding mode control for a class of nonlinear uncertain state-delayed systems with immeasurable states. A novel switching surface is proposed and a state observer is employed to reconstruct the sliding mode control action. The proposed method does not need a priori knowledge of upper bounds on the norm of the uncertainties, but estimates them by using the adaptation technique so that the reaching condition can be satisfied. Based on Lyapunov stability theorem and linear matrix inequality (LMI) technique, the stability of the overall closed-loop nonlinear uncertain state-delayed system is guaranteed for the proposed control scheme under certain conditions. Furthermore, the state observer and control law can be constructed from the positive-definite solutions of two LMIs, and the design technique is simple and efficient. The validity of the proposed control methodology is demonstrated by simulation results.
The study on nonlinear control system has received great interest from the international research field of automatic engineering. There are currently some alternative and complementary methods used to predict the behavior of nonlinear systems and design nonlinear control systems. Among them, characteristic modeling (CM) and fuzzy dynamic modeling are two effective methods. However, there are also some deficiencies in dealing with complex nonlinear system. In order to overcome the deficiencies, a novel intelligent modeling method is proposed by combining fuzzy dynamic modeling and characteristic modeling methods. Meanwhile, the proposed method also introduces the low-level learning power of neural network into the fuzzy logic system to implement parameters identification. This novel method is called neuro-fuzzy dynamic characteristic modeling (NFDCM). The neuro-fuzzy dynamic characteristic model based overall fuzzy control law is also discussed. Meanwhile the local adaptive controller is designed through the golden section adaptive control law and feedforward control law. In addition, the stability condition for the proposed closed-loop control system is briefly analyzed. The proposed approach has been shown to be effective via an example.
A direct adaptive fuzzy control algorithm is developed for a class of uncertain SISO nonlinear systems. In this algorithm, it doesn’t require to assume that the system states are measurable. Therefore, it is needed to design an observer to estimate the system states. Compared with the numerous alternative approaches with respect to the observer design, the main advantage of the developed algorithm is that on-line computation burden is alleviated. It is proven that the developed algorithm can guarantee that all the signals in the closed-loop system are uniformly ultimately bounded and the tracking error converges to a small neighborhood around zero. The simulation examples validate the feasibility of the developed algorithm.
In this paper, we propose a novel feature extraction method for the identification of humans. The main objective of our method is to identify each human being by extracting the Gabor feature based on the Adaptive Motion Model (AMM) for the motion of humans. In our method, the adaptive motion model, which can represent the temporal motion for each walking human is first made from the sequence images and, then, the Gabor features of the eight directions which can represent the spatial motion information for humans are extracted. The proposed feature extraction method can make a more accurate motion model by adjusting the weight between the previous and current model for each person. Moreover, our method has the advantage of allowing more information such as the Gabor features for the eight directions extracted from the AMM. Since the conventional method uses the face feature for each human being, it has disadvantages in the case of images of small size, while our method has better identification performance this case, because it only uses the spatio-temporal motion information. Finally, we identify each person by finding the minimum value of the extended dynamic time warping (DTW) for the eight Gabor features. The accuracy of the identification conducted using the proposed feature is better than that of the conventional method using the Gait Energy Image (GEI) and Face Image feature.
A class of nonlinear singularly perturbed systems can be approximated by the Fuzzy Singularly Perturbed Model (FSPM).This paper proposes a new direct adaptive controller on the basis of the FSPM. The aim is to make the states of the closed-loop system follow those of the reference model. The feedback gains of the controller can be adjusted on line; and we don’t require the parameters known in prior. Lyapunov constitute techniques are used to prove the stability of the closed loop systems. Finally the simulations illustrate the effectiveness of this approach. KeywordsAdaptive control-Lyapunove constitute techniques-nonlinear singularly perturbed system-T-S fuzzy logic system
The well-known conventional Kalman filter gives the optimal solution but to do so, it requires an accurate system model and exact stochastic information. However, in a number of practical situations, the system model and the stochastic information are incomplete. The Kalman filter with incomplete information may be degraded or even diverged. To solve this problem, a new adaptive fading filter using a forgetting factor has recently been proposed by Kim and co-authors. This paper analyzes the stability of the adaptive fading extended Kalman filter (AFEKF), which is a nonlinear filter form of the adaptive fading filter. The stability analysis of the AFEKF is based on the analysis result of Reif and co-authors for the EKF. From the analysis results, this paper shows the upper bounded condition of the error covariance for the filter stability and the bounded value of the estimation error. Keywords: Adaptive Kalman filter, forgetting factor, nonlinear filter, stability analysis.
Top-cited authors
Lee Sangmin
  • Pusan National University
Joongseon Joh
  • Changwon National University
Moon Kyou Song
  • Wonkwang University
Hyungbo Shim
  • Seoul National University
Myo Taeg Lim
  • Korea University