Feedback Fault-tolerant Control Research Articles

Integral Sliding Mode Output Feedback Control for Unmanned Marine Vehicles Using T–S Fuzzy Model with Unknown Premise Variables and Actuator Faults

This paper addresses integral sliding mode output feedback fault-tolerant control (FTC) of unmanned marine vessels (UMVs) with unknown premise variables and actuator faults. Due to the complexity of the marine environment, the presence of uncertainties in the yaw angle renders the premise variables in the Takagi–Sugeno (T–S) fuzzy model of UMVs unknown. Consequently, traditional integral sliding mode techniques become infeasible. To address this issue, a control strategy combining integral sliding mode based on output feedback with a compensator utilizing switching mechanisms is proposed. First, a radial basis function neural network is used to approximate the nonlinear terms in the UMV T–S fuzzy model. In addition, an integral sliding mode surface is constructed based on fault estimation information and membership function estimation. On this basis, an FTC scheme based on integral sliding mode output feedback is developed to ensure that the UMV system is asymptotically stable and satisfies the prescribed H∞ performance index. Finally, simulation results are provided to demonstrate the effectiveness of the presented control strategy.

Adaptive Fuzzy Output Feedback Fault-Tolerant Control for Active Suspension Systems

In this paper, a novel adaptive fuzzy output feedback fault-tolerant control (FTC) method is proposed for active suspension systems. The quarter active suspension systems studied in this paper contains electromagnetic actuator faults and unavailable state variables, such as the velocity of active suspensions and the electric current intensity of electromagnetic actuators. First, the complex spring nonlinear dynamics are approximated by fuzzy logic systems (FLSs), and the unmeasurable states are estimated by a fuzzy state observer. Based on an adaptive backstepping technique along with specific Lyapunov functions, a novel adaptive fuzzy output feedback FTC design is presented. To eliminate the need for knowing efficiency indexes, a fault compensation strategy is also developed by utilizing parameter estimation technique. In the presence of electromagnetic actuator failure, all vertical vibration states of the active suspension system remain stabilized. Eventually, the feasibility of the proposed control algorithm is evaluated through random road surface and bump road surface tests.

Robust fault estimation and proportional derivative fault tolerant control for a class of singular systems with interval time‐varying delay and disturbance

SummaryThis paper deals with observer‐based fault estimation (FE) and proportional derivative state feedback fault tolerant control (FTC) schemes for singular systems affected by actuator fault, interval time‐varying delay and external disturbance. Firstly, the system transformation method of constructing the derivative matrix into a full‐rank matrix is adopted to transform the singular system into a normal system, which effectively eliminates the impulse characteristic of the singular system. Secondly, in the design of the dynamic observer, fault estimation utilizes the real‐time output error and its derivative simultaneously, which can achieve better estimation performance when the fault change frequently. Most importantly, the observer‐based FTC adopts a proportional derivative state feedback control, considers disturbance and interval time‐varying delay, and the introduction of state estimation derivative term can eliminate the impulse property of the closed‐loop system, which optimizes the design scheme of the fault‐tolerant controller. The obtained sufficient conditions for stability can be solved by the strict linear matrix inequality (LMI), and the superiority of the conclusions will be further verified by the simulations.

Adaptive dynamic output feedback FTC for nonlinear systems with unknown nonlinearities and faults

AbstractThe problem of adaptive dynamic output feedback fault tolerant control (FTC) for a class of nonlinear systems with unknown nonlinearities and faults is investigated, in which the nonlinearities do not need to satisfy the Lipschitz condition, so the system can represent a wider range of nonlinear system models. An adaptive dynamic output feedback FTC is designed, where the output and its derivatives are fully applied to compensate the actuator faults on systems. The designed control method can effectively offset the effect of faults on systems so as to stabilized the nonlinear system. Furthermore, based on the designed controller, the coupling term can be eliminated reasonably and less conservative stability conditions for the closed‐loop system have been obtained. The proposed control strategy pledges that the states of the closed‐loop system are uniformly ultimately bounded (UUB). Two numerical examples are presented to exhibit the effectiveness and merits of the proposed control scheme.

Adaptive output feedback attitude control for reusable launch vehicle with input constraints and actuator faults

This paper considers an adaptive output feedback fault tolerant control (FTC) for a reusable launch vehicle (RLV) during reentry phase for which four grid fins are used as actuators. Based on a prescribed tracking performance and an auxiliary system to deal with the input constraints, the proposed adaptive controller can compensate for the effect of uncertainties, external and internal disturbances, input constraints and actuator faults. It is shown that with the proposed scheme, the system tracking performance can be guaranteed even in the presence multiple actuator faults. The influence of the input constraints on the tracking performance is also investigated. Simulation results are presented to illustrate the effectiveness of the proposed scheme.

Neural Network-Based Output Feedback Fault Tolerant Tracking Control for Nonlinear Systems with Unknown Control Directions

In this study, an adaptive output feedback fault tolerant control (FTC) scheme is proposed for a class of multi-input and multioutput (MIMO) nonlinear systems with multiple constraints. The neural network (NN) is adopted to handle the unknown nonlinearity by means of its superior approximation capability. Based on it, the state observer is designed to estimate the unmeasured states, and the nonlinear disturbance observer is constructed to tackle the external disturbances. In addition, the Nussbaum function is utilized to cope with the actuator faults, which are coupled with the unknown control directions. Combining with the Lyapunov theory, a NN-based output feedback FTC law is developed for the MIMO nonlinear systems, and the boundedness of all closed-loop system error signals is proved. Simulation results on the unmanned helicopter are performed to demonstrate the effectiveness of the proposed controller.

Adaptive output feedback fault‐tolerant control for nonlinear stochastic systems with output constraint

AbstractThis article focuses on the adaptive output feedback fault‐tolerant control (FTC) problem for nonlinear stochastic systems with output constraint. Different from the existing FTC results, the actuator replacement time is considered, which may cause the tracking error to violate the constraint boundaries. First, because only inaccurate output information is available, a dynamic gain‐based reduced‐order K‐filter is designed to reconstruct unmeasurable state variables. Second, by means of a shifting function, a funnel‐like constraint function is proposed to constrain the tracking error into the pre‐given asymmetric constraint boundaries within a predefined time. Third, monitoring function is proposed to detect actuator failures. Once the tracking error violates the monitoring function, it is determined that the actuator failure has occurred, and then the backup actuator driven by the recovery controller will replace the failed actuator. With the help of Lyapunov stability theory, it is strictly proved that all closed‐loop system signals are bounded in probability. Finally, the simulation result is presented to verify the validity of the proposed algorithm.

Adaptive output feedback fault tolerant control for uncertain nonlinear systems based on actuator switching

In this paper, an adaptive output feedback fault tolerant control (FTC) based on actuator switching is proposed for a class of single-input single-output (SISO) nonlinear systems with uncertain parameters and possible actuator failures, for which a set of healthy actuators are available as backups. While high-gain K-filters are utilized to estimate the unmeasured states, an adaptive control law is designed to compensate for the parameter uncertainties and certain actuator failures, an actuator switching strategy based on a set of appropriately designed monitoring functions (MFs) is proposed to tackle those serious actuator failures, make tracking error satisfy prescribed transient and steady-state performance and guarantee closed-loop signal boundedness.

A new separated fault estimator and fault-tolerant control design strategy for uncertain nonlinear systems using T-S fuzzy modeling

In this paper, a new separated design of adaptive observer-based fault estimator (FE) and fault-tolerant control (FTC) is introduced for nonlinear systems along with sensor and actuator faults in the presence of external disturbance and parametric modeling uncertainty by T-S fuzzy modeling, which can easily challenge integrated design strategies. Firstly, a new fuzzy adaptive FE estimates actuator/sensor faults, and the system states simultaneously and also can automatically estimate and compensates bi-directional uncertainties between FE function and control system. Secondly, a fuzzy state feedback FTC controller is designed based on these estimations. Then the proposed separated FE/FTC design presented which is solved by linear matrix inequality via $H_infty$ optimization. Finally, a tutorial example of an inverted pendulum on a cart demonstrates the validity and effectiveness of the proposed design.

Output to state description-based convergent fault tolerant control for linear systems with actuator faults

This paper focuses on the output to state description-based convergent fault tolerant control (FTC) for a class of linear time invariant (LTI) systems. An output to state description theorem is proposed, by which, an adaptive output feedback FTC with a function equation constraint is designed for the LTI system in consideration. A controller reconfiguration algorithm is proposed to solve the constraint and construct an approximate controller that can compensate effectively the fault effect on systems. Furthermore, in certain conditions, the output feedback control gains can be determined by the state feedback control gains. A linear longitudinal dynamics of an aircraft and a linearised dynamic model of a vertical takeoff and landing (VTOL) aircraft in the vertical plane are applied to illustrate the effectiveness and merits of the proposed method.

Adaptive fuzzy output feedback FTC for nonstrict-feedback systems with sensor faults and dead zone input

This paper solves the fault-tolerant control (FTC) problem of uncertain nonlinear systems in nonstrict-feedback form. The controlled plants considered in this paper are more complicated than existing ones, which is composed of the unknown nonlinearities, unmeasured states, and sensor faults and unknown dead zone input nonlinearity. Fuzzy logic systems and a fault-estimation-based state observer are used for identifying the unknown nonlinearities and obtaining the unmeasured states, respectively. By designing the sensor faults compensation and the dead zone inverse compensation methods, an observer-based fault-tolerant compensation control scheme is developed under backstepping recursive design frame. And it is testified that the closed-loop signals are bounded, and the tracking performance is satisfied even when the controlled systems are not free of sensor faults and unknown dead zone input nonlinearity. An electromechanical system is used to test the effectiveness of the developed control strategy.

Minimum-Learning-Parameters-Based Adaptive Neural Fault Tolerant Control With Its Application to Continuous Stirred Tank Reactor

In this paper, a decentralized neural network (NN) output feedback fault tolerant control (FTC) problem is addressed for a class of multi-input multi-output systems with actuator fault. In order to avoid the noncausal problem, the original system is transformed into an input-output expression in accordance with the diffeomorphism theory. Then, in order to establish a quick response to the fault, the fault tolerant controller with minimum learning parameters has been designed such that the semiglobal uniform ultimate boundedness of all the variables in the resulting closed-loop systems can be guaranteed. Finally, the output feedback FTC approach is applied to the interconnected CSTRs, and the comparisons with existing methods are provided to show the effectiveness of the proposed strategy.

Output Feedback Fault‐Tolerant Control of Heterogeneous Multi‐Agent Systems

AbstractThis paper investigates the design of an output feedback fault‐tolerant control (FTC) scheme for heterogeneous multi‐agent systems subject to loss of effectiveness and time‐varying additive actuator faults as well as external disturbances. The main aim of the proposed FTC approach is to maintain the behavior of a multi‐agent system after the occurrence of faults and external disturbances similar to the desired healthy and undisturbed one. The proposed FTC scheme does not depend on the implemented mission of the multi‐agent systems and it can be easily augmented to the existing nominal controller of each agent. Simulation results corresponding to a team of AUVs demonstrate and illustrate the effectiveness of our proposed FTC scheme.

Dynamic Output Feedback Fault Tolerant Control for Unmanned Underwater Vehicles

In this paper, we propose a dynamic output feedback fault tolerant control (FTC) scheme to achieve trajectory tracking for an unmanned underwater vehicle (UUV) subject to actuator faults, actuator saturation, external disturbances and other system uncertainties. To make matters more challenging, it is assumed that the velocities (linear and angular) are unavailable. In this condition, the UUV motion model in the horizontal plane has a high relative degree, thus increasing the design complexity. In contrast to observer-based FTC schemes, we introduce a dynamic compensator, which produces an augmented error system whose dynamics is minimum phase and relative degree one. An adaptive learning observer is then introduced to address the composite uncertainties arising from actuator faults, uncertain nonlinearities, actuator saturation and disturbances. We show that the proposed scheme can ensure that the trajectory tracking errors converge to a small neighbourhood around zero. Finally, a simulation example is used to prove the efficacy of the proposed scheme.

Adaptive neural output feedback fault tolerant control for a class of uncertain nonlinear systems with intermittent actuator faults

In real applications, actuators of control systems frequently encounter unknown intermittent faults during operation while effectively handing such faults is still a challenge. In this paper, an adaptive neural output feedback fault tolerant control (FTC) scheme based on the command filtered backstepping is developed for a class of uncertain nonlinear systems to address this challenge. In this scheme, a stable nonlinear observer is designed to estimate the system states and neural networks with random hidden nodes are utilized in this observer to approximate unknown functions. A projection algorithm is adopted to estimate system unknown parameters such that the boundedness of parameter estimates is guaranteed. It is proved that the boundedness of all signals in the closed-loop system can be ensured by the proposed modified Lyapunov function. Also the ultimate bound of the tracking error depends on design parameters, adjustable jumping amplitude of Lyapunov function and minimum fault time interval. A truncated L2 bound is established by iterative calculation to illustrate that the transient tracking error performance is determined by design parameters in the controller and observer. Applications on two simulation examples validate the effectiveness of the proposed scheme.

Lower Triangle Factor-Based Fault Estimation and Fault Tolerant Control for Fuzzy Systems

This article focuses on the problems of observer-based fault estimation and dynamic output feedback fault tolerant control (FTC) for a class of nonlinear fuzzy systems with faults. A novel lower triangle factor-based estimation observer (LTFEO) is proposed for the first time. The designed LTFEO can reduce to the traditional robust estimation observer (REO) and is superior to the adaptive estimation observer (AEO) in the relevant literature, furthermore, the designed LTFEO does not contain the output derivatives. The initial estimation accuracy is improved under the designed estimation observer. An iterative estimation algorithm based on the iterative LTFEOs is given, by which, the obtained mean sequence of state and fault estimation errors can converge to zeros (vector) and the proof is given in theoretical. Then, a new dynamic output feedback FTC is designed to stabilize the faulty system, where the output feedback gains can be determined by state feedback gains and the computational difficulty is decreased. Less conservative stability conditions for the estimation error dynamics and the closed-loop system are given in terms of linear matrix inequalities (LMIs). A tunnel diode circuit system is applied to test the effectiveness and merits of the proposed methods.

Observer-Based Adaptive Output-Feedback Fault-Tolerant Control of a Class of Complex Dynamical Networks

This paper is concerned with the observer-based adaptive output-feedback fault-tolerant control (FTC) problem for a class of complex dynamical networks (CDNs) with actuator faults. First, to address the coupling terms involved in the node dynamics, algebraic graph theory is used to design a new distributed state observer. Then, an observer-based adaptive output feedback FTC scheme is given to achieve the desired synchronization performance. Furthermore, via the Lyapunov analysis approach, it is proved that both the state estimation errors and the synchronization errors are all bounded for the faulty cases, while the asymptotical convergence is ensured for the fault-free case. In addition, it is shown that the proposed FTC scheme is valid for CDN with both time-invariant and time-variant graphs. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.

Neural network-based adaptive output feedback fault-tolerant control for nonlinear systems with prescribed performance

In this paper, an adaptive fault-tolerant control (FTC) approach is proposed for nonlinear systems involving input saturation and unknown control directions. In the framework of dynamic output feedback FTC, neural networks (NNs) are used to approximate the unknown nonlinear functions, and then a novel adaptive controller is further designed by combining the Nussbaum function property. Unlike some existing works incorporating an auxiliary state variable, the asymmetric actuator saturation is handled by the Nussbaum function in this approach. Furthermore, it is proven that all signals in the closed-loop system are bounded and the output tracking error satisfies the prescribed performance condition through using the barrier Lyapunov function method. Finally, simulation examples are presented to illustrate the effectiveness of the proposed control method.

Adaptive estimation and output feedback FTC for nonlinear systems with unknown nonlinearities and faults

SummaryThe adaptive estimation mechanism and output feedback fault‐tolerant control (FTC) design is concerned for nonlinear systems with faults, where the unknown nonlinearities are not required to satisfy the Lipschitz condition. An intermediate variable–based adaptive output feedback FTC with an adaptive parameter estimator and an adaptive state tracker is designed. The designed controller can be updated online to compensate the effect of nonlinearities and process faults on systems so that the states of the closed‐loop transformed system are uniformly bounded. The control output is able to be adjusted online by the designed event‐triggering mechanism to maintain fluctuating within a prescribed small range. Numerical examples exhibit the simulation results under the proposed FTC.

Online adaptive policy iteration based fault-tolerant control algorithm for continuous-time nonlinear tracking systems with actuator failures

In this paper, a novel tracking control scheme for continuous-time nonlinear affine systems with actuator faults is proposed by using a policy iteration (PI) based adaptive control algorithm. According to the controlled system and desired reference trajectory, a novel augmented tracking system is constructed and the tracking control problem is converted to the stabilizing issue of the corresponding error dynamic system. PI algorithm, generally used in optimal control and intelligence technique fields, is an important reinforcement learning method to solve the performance function by critic neural network (NN) approximation, which satisfies the Lyapunov equation. For the augmented tracking error system with actuator faults, an online PI based fault-tolerant control law is proposed, where a new tuning law of the adaptive parameter is designed to tolerate four common kinds of actuator faults. The stability of the tracking error dynamic with actuator faults is guaranteed by using Lyapunov theory, and the tracking errors satisfy uniformly bounded as the adaptive parameters get converged. Finally, the designed fault-tolerant feedback control algorithm for nonlinear tracking system with actuator faults is applied in two cases to track the desired reference trajectory, and the simulation results demonstrate the effectiveness and applicability of the proposed method.