Decentralized finite-time neural control for time-varying state constrained nonlinear interconnected systems in pure-feedback form

This paper focuses on the problem of decentralized adaptive neural network finite-time control for pure-feedback nonlinear interconnected systems with input quantization and time-varying state constraints. Neural networks are used to model the unknown functions. To prevent the violation of state constraints, the time-varying barrier Lyapunov functions are employed in each step of the controller design. Mean-while, combining with adaptive backstepping control technique, a decentralized adaptive neural network finite-time control strategy is raised. It is testified that the proposed control scheme can effectively ensure that all the closed-loop signals are semi-global practical finite-time stable via Lyapunov stability analysis and that the tracking errors converge to small bounded sets around the origin in finite time. Finally, some simulation results are used to verify the effectiveness of the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.