In this article, the problem of decentralized full-state constrained adaptive tracking control is investigated for interconnected time delay systems with input saturation. The extreme learning machine is applied to approximate the unknown nonlinear function in the design process. By selecting the appropriate Lyapunov–Krasovskii function, the time delay interconnection term could be compensated. A unified barrier function is introduced to ensure that the constraints imposed on all states are not violated. The Nussbaum function is introduced to deal with the problem of input saturation. By applying adaptive backstepping procedures, a novel decentralized full-state constrained adaptive tracking control strategy is proposed for the considered interconnected time delay systems. In terms of Lyapunov stability theory, it is proved that all the signals of the closed-loop systems are semi-global ultimately uniformly bounded and all state variables could be constrained within the time varying asymmetric boundaries. Finally, the effectiveness of the proposed scheme is illustrated by a simulation example of a two-stage chemical reactor.
Neural Networks-Based Adaptive Tracking Control for a Class of High-Order Stochastic Nonlinear Time-Delay Systems
