This article develops the performance recovery-based state-triggered fault-tolerant control (FTC) strategy for nonlinear interconnected systems (NISs). Under discontinuous state transmission, a novel set of event-triggered chainlike filters is introduced to reconstruct the error coordinate transformation, effectively addressing the challenge of state-triggered nondifferentiability. Then, a dynamic redundancy fault-tolerant control method is presented, which is tailored to constrain full errors and ensure prescribed performance, even in the face of actuator faults. It is capable of handling different types of faults, including moderate faults and extreme faults, and explicitly takes deferred actuator switching into account. By exploiting the performance function, the exceeding errors can be recovered into the safe constraints even after complete actuator failure. Besides, the globally uniformly ultimately bounded of closed-loop signals is realized without dependence on any unknown initial conditions. Finally, the validity of the proposed method is confirmed by a practical example.

This article discusses the adaptive fuzzy asymptotic tracking control for high-order nonlinear time-delay systems with full-state constraints. Fuzzy-logic systems and a separation principle are utilized to relax growth assumptions imposed on unknown nonlinearities. The adverse effect caused by unknown time delays is eliminated by choosing appropriate Lyapunov-Krasovskii functionals. By integrating nonlinear-transformed functions with a key coordinate transformation into the control design and constructing a specific compact set on the initial values of system states, the desired trajectory and parameter estimates, it is rigorously proved that all closed-loop signals are semiglobally bounded, the fuzzy approximation is valid, the full-state constraints are not violated without feasibility conditions on virtual controllers, and asymptotic tracking is achieved. The effectiveness and advantages of this control scheme are confirmed by two examples including a single-link robotic system.

In this article, an adaptive practical tracking control scheme is presented for full-state constrained high-order nonlinear systems. By skillfully introducing the adaptive gain, nonlinear transformed functions and sign functions into control design, a novel continuous state-feedback controller is constructed without imposing restrictive approximation techniques and feasibility conditions. Under mild assumptions, the boundedness of all the closed-loop signals can be guaranteed, full-state constraints are not transgressed for all time, and the tracking error tends to an arbitrarily small region of zero in a finite time.

This article proposes an adaptive neural network (NN) control method for an n -link constrained robotic manipulator. Driven by actual demands, manipulator and actuator dynamics, state and input constraints, and unknown time-varying delays are taken into account simultaneously. NNs are employed to approximate unknown nonlinearities. Time-varying barrier Lyapunov functions are utilized to cope with full-state constraints. By resorting to saturation function and Lyapunov-Krasovskii functionals, the effects of actuator saturation and time delays are eliminated. It is proved that all the closed-loop signals are semiglobally uniformly ultimately bounded, full-state constraints and actuator saturation are not violated, and error signals remain within compact sets around zero. Simulation studies are given to demonstrate the validity and advantages of this control scheme.

This article proposes an adaptive dynamic surface fuzzy control method for a class of nonstrict feedback systems. The systems are subject to state constraints with unknown control directions, time-delay, and external disturbances. The problem of unknown control direction is solved by using the Nussbaum gain technique, and the effect of unknown delay which is time varying is eliminated by introducing compounding type Lyapunov function. Furthermore, based on the fuzzy backstepping method, an adaptive tracking controller is constructed which guarantees that the constrained states are not violated while all the closed-loop trajectory signals remain bounded. The effectiveness of the obtained results is illustrated via simulations.

In this paper, for uncertain nonlinear systems with time-varying full state constraints, input saturation and unknown control direction, time-varying asymmetric barrier Lyapunov functions, the auxiliary subsystem, and the Nussbaum gain technique are employed. It is shown that all the closed-loop signals are bounded in the semi-global sense, error signals converge to bounded compact sets, and time-varying full state constraints and input saturation are not violated.

This paper investigates adaptive fuzzy tracking control for high-order nonlinear time-delay systems with full-state constraints and input saturation. By adopting fuzzy approximation technique, frequently used growth assumptions imposed on unknown system nonlinearities are removed. High-order barrier Lyapunov functions are employed to prevent the transgression of full-state constraints. The auxiliary subsystem and the Nussbaum gain technique are utilized to analyze the effect of input saturation. Appropriate Lyapunov-Krasovskii functionals are constructed to compensate the adverse effect caused by the unknown time-delay. Novel feasibility conditions are formulated as sufficient conditions for the existence of proposed control. It is rigorously proved that all the closed-loop signals are bounded in the semi-global sense, error signals converge to small bounded compact sets, full-state constraints, and input saturation are not violated, and the arguments of unknown nonlinearities are constrained within a compact set, on which fuzzy approximation is valid. Eventually, the theoretical result is confirmed by two simulation examples.