Adaptive Intelligent Controller Design-Based ISS Modular Approach for Uncertain Nonlinear Systems with Time-Varying Full State Constraints

The present paper focuses on an input-to-state stability (ISS) problem of nonlinear strict feedback system with time-varying full-state constraints. The adaptive Neural controllers are designed by using back-stepping, barrier Lyapunov function (BLF) and ISS small gain approach. BLF is proved to satisfy small-gain condition, thus the conception of input-to-state stable time-varying BLF (ISSTVBLF) is produced. Also, the ISSTVBLF is novel imbedded into the back-stepping design for a subsystem, which can prevent the full-state constraints from being violated for all the time, and avoid the designed difficulties resulting from the requirement of entire BLF for the time-varying constrained system. Neural network approximate technique is utilized to estimate unknown nonlinear functions. Then, a systematic procedure is developed to derive new adaptive tracking controllers based on small-gain theorem. It is proved that all the closed-loop signals are semi-global uniform ultimate boundedness and the tracking error is driven to a small domain around zero. Finally, simulation study results illustrate the effectiveness of proposed control schemes.