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The finite-time dynamic event-triggered fuzzy output feedback fault-tolerant control problem is studied in this article for the interval type-2 (IT2) Takagi–Sugeno fuzzy system with parameter uncertainties and actuator faults. A fuzzy state observer is first developed to solve the immeasurable state problem. Second, by using the sampled estimating states and measured output signals, a dynamic event-triggered mechanism is formulated via integrating sensor-to-observer with observer-to-controller. Third, an observer-based finite-time event-triggered fuzzy fault-tolerant controller is synthesized via the nonparallel distribution compensation design principle. Consequently, the finite-time stable conditions of the addressed IT2 fuzzy system are established by constructing an appropriate Lyapunov function. Furthermore, an output feedback control design algorithm of solving control and observer gains is given in terms of the established sufficient finite-time stable conditions. Finally, a practical example of a nonlinear tunnel diode circuit system is provided to verify the effectiveness of the proposed IT2 fuzzy control scheme. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive control problem for a class of uncertain fractional nonlinear systems with actuator faults, where the total number of failures is allowed to be infinite. A compensating term in a smooth function form of a conventional control law is introduced to compensate for the actuator faults. After the introduction of fractional-order adaptation laws, an adaptive controller is designed by using a modified backstepping technique. Fractional Lyapunov stability criterion is adopted to prove the convergence of the developed proposed controller even in the presence of actuator faults. Finally, a simulation example of fractional-order Chua–Hartley’s system is given to verify the effectiveness of the proposed fault-tolerant control scheme. [ABSTRACT FROM AUTHOR]

In this paper, the observer-based fully distributed event-triggered adaptive fuzzy containment fault-tolerant control (FTC) problem is studied for nonlinear multi-agent systems (MASs) subject to unknown actuator faults. Fuzzy logic systems (FLSs) are utilized to model unknown agents, and a new state observer is constructed to estimate unmeasurable states. Since the global communication information of topology is not available, the local reference signals are also not available, a distributed estimator is introduced to obtain the information of the local reference signals. To deal with the unknown actuator faults, a novel actuator fault compensation mechanism with a projection operator is adopted. Then based on backstepping technique and by designing a two-channel event-triggered mechanism, a fully distributed event-triggered containment FTC approach is developed. It is testified that all parameters and variables of the controlled system are bounded, and the followers can converge to the dynamic convex hull spanned by the leaders. Finally, we apply the developed control algorithm to the multiple unmanned surface vehicle (USV) systems, and the corresponding simulation results further confirm the feasibility of the developed control algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive neural network (NN) controller is proposed for a class of nonlinear active suspension systems (ASSs) with hydraulic actuator. To eliminate the problem of “explosion of complexity” inherently in the traditional backstepping design for the hydraulic actuator, a dynamic surface control technique is developed to stabilize the attitude of the vehicle by introducing a first-order filter. Meanwhile, the presented scheme improves the ride comfort even when the uncertain parameter exists. Due to the existence of uncertain terms, the NNs are used to approximate unknown functions in the ASSs. Finally, a simulation for a servo system with hydraulic actuator is shown to verify the effectiveness and reliability of the proposed approach. [ABSTRACT FROM AUTHOR]

In this article, we study the control problem of the vehicle active suspension systems (ASSs) subject to actuator failure. An adaptive control scheme is presented to stabilize the vertical displacement of the car-body. Meanwhile, the ride comfort, road holding, and suspension space limitation can be guaranteed. In order to overcome the uncertainty, the neural network is developed to approximate the continuous function with the unknown car-body mass. Furthermore, to improve the transient regulation performance of ASSs when the actuator failure occurs, we propose a novel control scheme with the prescribed performance function to characterize the tracking error convergence rate and maximum overshoot in ASSs. Then, the stability of the proposed control algorithm can be proven based on the Lyapunov theorem. Finally, the comparative simulation results of two actuator failure types (i.e., the float fault and the loss of effectiveness fault) are given to demonstrate the effectiveness of the proposed control schemes. [ABSTRACT FROM AUTHOR]

This article studies the fixed-time fuzzy formation tracking control problem for multiple unmanned surface vehicle (USV) systems with intermittent actuator faults. Since each USV is subject to unknown motion dynamics and the external disturbances. The fuzzy state observers are developed to estimate the positions and velocity in multiple USV systems, and disturbance observers are developed to estimate the external disturbances, and fuzzy logic systems are utilized to identify the unknown motion dynamics. Then, to compensate for the influence of intermittent actuator faults, the novel fixed-time adaptive output-feedback formation fault-tolerant controllers are constructed by constructing the integral type Lyapunov function and co-designing the last virtual controller. The presented formation control method can guarantee all the signals in multiple USV systems are bounded in fixed-time. Finally, simulation results and comprehensive comparisons are provided to verify the effectiveness of the presented formation control method. • This paper proposes an observer-based fuzzy formation FTC approach for multiple nonlinear USV systems. The presented formation FTC can achieve to compensate for the influence of IAFs. Although the recent works (Wang, 2021 ; Zhang et al., 2022) also address the FTC problem for nonlinear USVs, their FTC method only deal with a finite of actuator fault and can not be applied to control nonlinear multiple USV systems with immeasurable states in this study. [ABSTRACT FROM AUTHOR]

Summary: This article considers the issue of fuzzy adaptive dynamic programming control of nonlinear switched systems with arbitrary switchings and unknown uncertain functions and actuator hysteresis nonlinearities. The whole control approach is made of switching feedforward controller and optimal switching feedback controller. To get over the hardness of arbitrary switching structure and the issue of "explosion of complexity", the common Lyapunov function theory and dynamic surface control method are utilized in the recursive design technique. By using fuzzy logic systems to model unknown inner dynamics and unknown cost functions, a novel fuzzy adaptive optimal switching control strategy is developed. Meanwhile, uniformly ultimately boundedness of all weights in the controlled systems are proved by the proposed control method, and the tracking performance is guaranteed in an optimal manner. Subsequently, a numerical simulation study is used to test the effectiveness of the presented control strategy. [ABSTRACT FROM AUTHOR]

This paper investigates adaptive fuzzy output feedback fault-tolerant optimal control problem for a class of single-input and single-output nonlinear systems in strict feedback form. The considered nonlinear systems contain unknown nonaffine nonlinear faults and unmeasured states. Fuzzy logic systems are used to approximate cost function and unknown nonlinear functions, respectively. It is assumed that the states of the systems to be controlled are unmeasurable, thus an adaptive state observer is developed. To solve the nonaffine nonlinear fault control design problem, filtered signals are introduced into the adaptive backstepping control design procedures, and in the framework of adaptive critic technique and fault-tolerant control technique, a novel adaptive fuzzy fault-tolerant optimal control scheme is developed. The stability of the closed-loop system is proved by using Lyapunov stability theory. The simulation results verify the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy fault-tolerant control approach is proposed for a class of nontriangular structure nonlinear systems, which contain immeasurable states and unknown actuator faults (actuator loss-of-effectiveness and bias fault). It should be noted that if the existing approaches are employed for nontriangular structure nonlinear systems, the algebraic loop problem may occur. In this study, fuzzy logic systems are employed to approximate unknown nonlinear functions, and a fuzzy state observer is designed to estimate immeasurable states. Then, based on the property of performance function and simple barrier Lyapunov function design method, the prescribed output tracking error dynamic performance and the corresponding stability are guaranteed. By using the parameter estimation technique, a new fault compensation strategy is developed to relax the requirement that the efficiency indicator must be known. The stability of the closed-loop system is proved by using the Lyapunov function stability theory. Finally, a simulation example is given to validate the effectiveness of the proposed control strategy. [ABSTRACT FROM AUTHOR]

This paper studies the adaptive fuzzy bounded control problem for leader–follower multiagent systems, where each follower is modeled by the uncertain nonlinear strict-feedback system. Combining the fuzzy approximation with the dynamic surface control, an adaptive fuzzy control scheme is developed to guarantee the output consensus of all agents under directed communication topologies. Different from the existing results, the bounds of the control inputs are known as a priori, and they can be determined by the feedback control gains. To realize smooth and fast learning, a predictor is introduced to estimate each error surface, and the corresponding predictor error is employed to learn the optimal fuzzy parameter vector. It is proved that the developed adaptive fuzzy control scheme guarantees the uniformly ultimate boundedness of the closed-loop systems, and the tracking error converges to a small neighborhood of the origin. The simulation results and comparisons are provided to show the validity of the control strategy presented in this paper. [ABSTRACT FROM PUBLISHER]

This paper deals with the problem of adaptive fuzzy tracking control for a single-link robotic manipulator coupled to a brushed direct current motor with actuator saturation. Fuzzy logic systems are used to approximate the unknown nonlinear systems. The filtered signals are introduced to eliminate the interference of high-frequency signals. The virtual/actual control inputs are derived from the solutions of a series of dynamical equations. Under the framework of the backstepping control design, an observer-based output feedback control design scheme is proposed. It is proved that the control approach can guarantee that all the signals in the closed-loop system are bounded. [ABSTRACT FROM AUTHOR]

The problem of adaptive fuzzy output-constrained tracking fault-tolerant control (FTC) is investigated for the large-scale stochastic nonlinear systems of pure-feedback form. The nonlinear systems considered in this paper possess the unstructured uncertainties, unknown interconnected terms and unknown nonaffine nonlinear faults. The fuzzy logic systems are employed to identify the unknown lumped nonlinear functions so that the problems of structured uncertainties can be solved. An adaptive fuzzy state observer is designed to solve the nonmeasurable state problem. By combining the barrier Lyapunov function theory, adaptive decentralized and stochastic control principles, a novel fuzzy adaptive output-constrained FTC approach is constructed. All the signals in the closed-loop system are proved to be bounded in probability and the system outputs are constrained in a given compact set. Finally, the applicability of the proposed controller is well carried out by a simulation example. [ABSTRACT FROM PUBLISHER]

This paper investigates the problem of fault-tolerant control (FTC) for a class of switched nonlinear systems. These systems are under arbitrary switchings and are subject to both lock-in-place and loss-of-effectiveness actuator faults. In the control design, fuzzy logic systems are used to identify the unknown switched nonlinear systems. Under the framework of the backstepping control design, FTC, fuzzy adaptive control and common Lyapunov function stability theory, an adaptive fuzzy control approach is developed. It is proved that the proposed control approach can guarantee that all the signals in the closed-loop switched system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error remains an adjustable neighbourhood of the origin. Two simulation examples are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper investigates the robust stabilization problem for a class of fuzzy Takagi–Sugeno (T–S) switched system with actuator dead-zone. By using parallel distributed compensation (PDC) design scheme, a robust fuzzy control law and a switching law is also constructed via using a single Lyapunov function method, respectively. The sufficient conditions of ensuring the switched fuzzy control system asymptotic stabilization are proposed and parameterized in terms of linear matrix inequalities (LMIs), which can be solved very efficiently using the convex optimization techniques. It is proved that proposed control scheme can guarantee that whole closed-loop system is asymptotically stable in the sense of the Lyapunov function. A numerical example is given to illustrate the effectiveness of the proposed control method. [ABSTRACT FROM AUTHOR]

This paper is concerned with the problem of type-2 fuzzy adaptive inverse control for a cable-driven parallel system. Based on the heuristics and prior knowledge of the system, the system is divided into six subsystems. The proposed control scheme for each subsystem contains a forward model and a fuzzy adaptive inverse controller (FAIC), which are expressed by an interval type-2 fuzzy nonlinear autoregressive exogenous (NARX) model, respectively. To construct the antecedents of the interval type-2 fuzzy NARX forward models and FAICs, the monotonic property of the fuzzy NARX model is first proved, and then, their antecedent parameters can be determined by this property. Furthermore, the consequent parameters of the forward models are computed offline via a constrained least squares algorithm, and the consequent parameters of the FAIC are adjusted online via a recursive least squares algorithm. Experiment results are provided to show that the proposed type-2 fuzzy control scheme can realize the control objectives and achieve a good control performance. [ABSTRACT FROM PUBLISHER]

This paper discusses the adaptive fuzzy decentralised fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The systems under study contain the unknown nonlinearities, unmodelled dynamics, actuator faults and without the direct measurements of state variables. With the help of fuzzy logic systems identifying the unknown functions and a fuzzy adaptive observer is designed to estimate the unmeasured states. By using the backstepping design technique and the dynamic surface control approach and combining with the changing supply function technique, a fuzzy adaptive FTC scheme is developed. The main features of the proposed control approach are that it can guarantee the closed-loop system to be input–to-state practically stable, and also has the robustness to the unmodelled dynamics. Moreover, it can overcome the so-called problem of ‘explosion of complexity’ existing in the previous literature. Finally, simulation studies are provided to illustrate the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

This study investigates the adaptive fuzzy fault‐tolerant control (FTC) problem for a class of uncertain non‐linear strict‐feedback systems with unmeasured states. The considered non‐linear systems contain unknown continuous functions and do not satisfy the matching condition. The actuator failures under study are types of both abrupt faults and lock‐in‐place and loss of effectiveness. The fuzzy logic systems are employed to approximate the unknown continuous functions, and a fuzzy state observer is developed and the unmeasured states are obtained. Under the framework of the backstepping design technique and incorporated by the dynamic surface control approach and predefined performance bounds, an adaptive fuzzy FTC method has been presented. From the Lyapunov stability analysis, it is shown that all the signals of the resulting closed‐loop system are bounded and the tracking error surfaces remain within the prescribed performance bounds in the presence of unknown non‐linear actuator faults. The simulation results and comparisons with the previous methods indicate the effectiveness of the proposed adaptive fuzzy FTC. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive fuzzy decentralized fault-tolerant control (FTC) problem for a class of nonlinear large-scale systems in strict-feedback form. The considered nonlinear system contains the unknown nonlinear functions, i.e., unmeasured states and actuator faults, which are modeled as both loss of effectiveness and lock-in-place. With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is designed to estimate the unmeasured states. By combining the backstepping technique with the nonlinear FTC theory, a novel adaptive fuzzy decentralized FTC scheme is developed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded, and the tracking errors between the system outputs and the reference signals converge to a small neighborhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach. [ABSTRACT FROM AUTHOR]

In this paper, an adaptive fuzzy output feedback fault-tolerant control scheme is developed for a class of nonlinear systems with actuator faults, unmodeled dynamics and without assuming the states being available for control design. Fuzzy logic systems are employed to approximate unknown nonlinear functions, and a fuzzy state observer is established to estimate the unmeasured states. By combining adaptive backstepping technique with changing supplying function technique and the nonlinear fault-tolerant control theory, a novel adaptive fuzzy fault-tolerant output feedback control approach is developed. It is proved that proposed control approach guarantees that all the variables of the closed-loop system are bounded. The simulation results are provided to illustrate the effectiveness of the proposed control approach. Compared with the existing results, the main contributions of this paper are as follows: (1) the proposed control method removes the restrictive assumption that all the states of the controlled system be measured directly; and (2) the proposed control method cannot only compensate for the actuator faults, but also solve the problem of the unmodeled dynamics. [ABSTRACT FROM AUTHOR]

This article develops an adaptive fuzzy control method for accommodating actuator faults in a class of unknown nonlinear systems with unmeasured states. The considered faults are modelled as both loss of effectiveness and lock-in-place (stuck at unknown place). With the help of fuzzy logic systems to approximate the unknown nonlinear functions, a fuzzy adaptive observer is developed for estimating the unmeasured states. Combining the backstepping technique with the nonlinear tolerant-fault control theory, a novel adaptive fuzzy faults-tolerant control approach is constructed. It is proved that the proposed control approach can guarantee that all the signals of the resulting closed-loop system are bounded and the tracking error between the system output and the reference signal converges to a small neighbourhood of zero by appropriate choice of the design parameters. Simulation results are provided to show the effectiveness of the control approach. [ABSTRACT FROM AUTHOR]

This paper studies the neural network (NN) decentralized observer-based fault-tolerant control (FTC) design problem on the nonlinear interconnected fractional-order systems. The considered fractional-order systems have the non strict-feedback form and are subject to immeasurable states and intermittent actuator faults. Neural networks (NN) are employed to identify the unknown dynamics, and a NN decentralized observer is formulated to estimate unknown states. By constructing appropriate Lyapunov functions and using the backstepping dynamic surface control (DSC) technique, a NN observer-based decentralized FTC method is developed. It is proved that the developed NN decentralized FTC scheme can guarantee that the controlled interconnected fractional-order system is stable and the track errors can be made smaller. Finally, a practical simulated example is provided to check the validity of the NN decentralized FTC algorithm. [ABSTRACT FROM AUTHOR]

This paper investigates the adaptive neural network optimal output feedback control design problem for nonlinear continuous-time systems with actuator saturation. The system dynamics and states of the controlled system are unknown. A neural network state observer is constructed to estimate the system states. This paper uses two neural networks, one is used to construct the neural network state observer, the other (critic neural network) is used to approximate the cost functions, which comprise an observer-critic architecture. In this architecture, the critic neural network weights are tuned based on both the current data and the previous data, thus the conditions of the persistent excitation in the previous literatures are relaxed. By utilizing adaptive dynamic programming approach, a new observer-based optimal control scheme is developed. It is proved that the proposed adaptive neural network output feedback optimal control scheme can ensure that the whole closed-loop system is stable. Moreover, the estimate errors of the critic neural network weights are asymptotically stable. A simulation example is given to validate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, the optimal fault-tolerant control (FTC) design problem is investigated for strict-feedback nonlinear systems. The considered nonlinear systems contain actuator faults, which are bias fault and gain fault. The problem of actuator faults is solved by utilizing the adaptive compensation technique. Applying the backstepping design technique, a main fault-tolerant controller is established. Via the designed main fault-tolerant control strategy, the controlled nonlinear system can be converted to an equivalent nonlinear system in affine form. Subsequently, a fuzzy logic system (FLS) is introduced to approximate the unknown cost function, and an adaptive fuzzy feedback optimal controller is developed. The whole adaptive fuzzy fault-tolerant optimal control scheme consists of a main controller and a feedback optimal controller. Based on the Lyapunov theory, it is proved that all the signals in the closed-loop system are bounded, the system output can track a bounded reference signal. In addition, the proposed control method can guarantee the cost function is the smallest. Simulation results are provided to illustrate the effectiveness of the proposed fault-tolerant optimal control scheme. [ABSTRACT FROM AUTHOR]

This paper deals with the decentralized control problem for a class of switched nonlinear large-scale systems with immeasurable states and actuator dead-zone. A switched state observer is designed to obtain the estimation of the immeasurable states. Under the parallel distributed compensation (PDC) design method, an output feedback decentralized controller is developed. In addition, the switching laws are also designed via using the multiple Lyapunov function method. The sufficient conditions of ensuring the closed-loop systems asymptotic stabilization are given and transformed into standard solvable linear matrix inequality (LMI). An example is provided to illustrate the effectiveness of the suggested theoretical results. [ABSTRACT FROM AUTHOR]