Improved Adaptive Fuzzy Control for Non-Strict Feedback Nonlinear Systems: a Dynamic Compensation System Approach

This paper investigates the tracking control problem for uncertain nonlinear non-strict feedback systems (NSFSs) in the presence of full-state constraints and unmeasured dis-turbances. It is of great practical significance to realize the full-state constraint under dis-turbed conditions. In view of the non-strict feedback problem, a novel design framework of the state feedback control is given based on the newly proposed dynamic compensation system (DCS). Different from the traditional backstepping, the estimated signal based on the adaptive fuzzy system is indirectly introduced into the virtual and actual control laws through the DCS, which has the advantage of avoiding the algebraic-loop problem in NS-FSs. Accordingly, the disturbance observer (DO) design method is improved based on this framework. Integrating the DCS into the DO design avoids the coupling problem between the disturbance and the unknown nonlinear function. An improved barrier Lyapunov func-tion (BLF) is designed by introducing the concept of the inducible factor, and full-state constraints can be guaranteed after a transitional period for any initial state. By combin-ing the DCS, the DO and the improved BLF, a novel adaptive fuzzy tracking control law is constructed, and all the signals in the closed-loop system are semiglobally uniformly ulti-mately bounded. Finally, the theoretical analysis and simulation results show that all states meet the corresponding constraints while meeting the stability.(c) 2022 Elsevier Inc. All rights reserved.

Automated summary

This paper investigates the tracking control problem for uncertain nonlinear non-strict feedback systems in the presence of full-state constraints and unmeasured disturbances. A novel design framework of state feedback control is proposed based on the dynamic compensation system and adaptive fuzzy system. The improved disturbance observer design and barrier Lyapunov function are integrated into the control law, guaranteeing the satisfaction of the full-state constraints. Theoretical analysis and simulation results demonstrate the effectiveness and stability of the proposed control algorithm.