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In this article, an adaptive neural fault-tolerant control (FTC) scheme is proposed for the medium-scale unmanned autonomous helicopter subject to external disturbance, actuator fault, and input saturation. Multiple approximators are constructed to handle the unknown terms and promote the control design. The nonlinear coupled function terms are approximated by virtue of the radial basis function neural networks. The unknown disturbance is tackled by the developed disturbance observer. Meanwhile, two auxiliary systems are introduced to handle the actuator fault and input saturation, respectively. In the framework of the backstepping method, a multiapproximator-based adaptive FTC strategy is presented, which assures the boundedness of all closed-loop system signals. Simulation results are presented to validate the availability of the designed controller. [ABSTRACT FROM AUTHOR]

This article investigates the sampled-data stabilization problem of a class of switched nonlinear systems. All subsystems of the considered system are allowed to be unstabilizable. To relax the restrictions on unknown nonlinear functions in some existing results, we use the nonlinear approximation ability of radial basis function neural networks. Novel mode-dependent adaptive laws and sampled-data control laws are constructed by only using the system states’ information at sampling instants. A novel sampled-data switching condition is derived, which can avoid Zeno behavior effectively. To guarantee that all states of the closed-loop system (CLS) are bounded, a new allowable sampling period is deduced. Finally, we demonstrate the proposed method’s effectiveness through two examples. [ABSTRACT FROM AUTHOR]

In this article, an adaptive approximation-based output-feedback tracking control scheme is presented for a class of stochastic switched lower-triangular nonlinear systems with input saturation and unmeasurable state variables. First, to overcome the design obstacle caused by the nondifferential saturation nonlinearity, a carefully selected nonlinear function of the control input signal is applied to estimate the saturation function. Then, a reduced-order state observer is designed to model the unmeasured system states, which also means the error system can be established. Furthermore, the fuzzy-logic systems are utilized to approximate the unknown system nonlinearities in the adaptive backstepping-based controller design procedure. It is ensured that all the closed-loop system variables are bounded in probability and the error signal belongs to a compact set in the mean square sense. Finally, the effectiveness and the practicability of the proposed control scheme are shown by two examples. [ABSTRACT FROM AUTHOR]

This paper, for the first time, presents an adaptive finite-time fuzzy funnel controller for nonaffine nonlinear systems via backstepping. To ensure the tracking error with prescribed boundedness, a modified transformation for funnel error is developed and embedded in the procedure of control design. The unknown packaged nonlinear functions appeared in the controller design process are approximated by using fuzzy logic systems. It is proved that the proposed method guarantees that the output tracking error falls within a preset funnel and all signals in the closed-loop system are semi-globally practically finite-time bounded (SGPFB). Simulations are carried out to demonstrate the effectiveness of the controller design scheme. [ABSTRACT FROM AUTHOR]

The problem of adaptive decentralized fault-tolerant control is investigated in this paper for the pure-feedback nonlinear switched large-scale systems. The considered systems possess the unknown compounded nonlinearities (unknown nonlinear functions, unknown interconnected terms, unknown nonlinear function faults, and unknown actuator dead-zone nonlinearity). The radial basis function neural networks are utilized for identifying the unknown compounded nonlinear functions so that the problems of unknown nonlinearities can be solved. A switched neural networks ${k}$ -filter observer is constructed to estimate the unmeasurable states, deal with the unknown actuator dead-zone nonlinearity and compensate the nonlinear function faults simultaneously. Then, by combining the average dwell time theory, adaptive decentralized control method, and under the framework of backstepping design, an adaptive output-feedback fault compensation control method is presented. Under the certain assumptions, the stability of the closed-loop system is proved by use of Lyapunov function stability theory. Finally, the applicability of the proposed controller is well carried out by a double-inverted-pendulums simulation example. [ABSTRACT FROM AUTHOR]

This paper addresses an error-driven nonlinear feedback design technique to improve the dynamic performance of fuzzy adaptive dynamic surface control (DSC) for a class of uncertain multiple-input-multiple-output nonlinear systems with prescribed tracking performance. The highlight of the error-driven nonlinear feedback technique is that the feedback gain self-regulates versus different levels of output and virtual tracking errors, this reflects the classical control design criterions commendably: relatively high feedback gains can be implemented to guarantee disturbances and uncertainties attenuation and so on to improve the control performance when small tracking errors are measured, and relatively small feedback gains can be implemented to circumvent the problems of actuator and states saturations when large tracking errors are measured. The complexity problem of the traditional backstepping design is circumvented owe to the peculiarity of DSC method. Caused by the compound error functions of nonlinear feedback dynamics, a nonquadratic Lyapunov function is used to deduce the conditions of closed-loop stability. Fuzzy logic systems and error transformation-based method are used in the online learning of completely unknown dynamics and the prescribed performance tracking, respectively. Comparative results are presented to demonstrate the effectiveness and preponderance of the proposed control scheme with comparison to existing ones. [ABSTRACT FROM AUTHOR]

The global asymptotic stabilization problem is investigated for a class of stochastic nonlinear time-varying delay systems under the weaker condition on nonlinear functions. The new parameter-dependent state and output feedback controllers are, respectively, proposed. Based on the stochastic time-delay system stability criterion, by tactfully introducing a suitable Lyapunov-Krasovskii functional, the globally asymptotically stable in probability of the closed-loop system is guaranteed by rigorous proof. As a practical application, the stochastic model of a two-stage chemical reactor system is established by reasonably introducing the Gaussian white noise. The developed approach is applied to the control design for this practical system. The simulation results demonstrate the efficiency of the proposed design approach. [ABSTRACT FROM AUTHOR]