Neuroadaptive PI control with prescribed performance for a class of nonaffine systems with non-smooth input saturation
Based on L1 adaptive control theory, a novel block backstepping control for a class of uncertain multiple-input-multiple-output nonlinear system is proposed. The matched system parametric uncertainty and unmatched general uncertainty including modeling error and external disturbance are considered in the design. The L1 adaptive control is integrated with block backstepping to improve the transient performance in addition to stable tracking. The low-pass filter is adopted to guarantee fast adaptive rate without generating high-frequency oscillations in control signal and ensure its smoothness. A reference system is introduced to prove the stability of the closed-loop system via L1 adaptive theory and Lyapunov theorem. Finally, numeral simulation results show the transient performance and feasibility of the proposed adaptive control. A neuroadaptive PI controller is developed for a class of nonaffine systems with bounded external disturbance and a symmetric non-smooth input saturation. Neural networks's (NNs's) capability of nonlinear function approximation and the use of exponentially decaying and hyperbolic functions in solving prescribed performance (PP) problems. Then, a transformed filtered error is designed to facilitate the control design and analysis. A stability analysis based on Lyapunov method guarantees uniformly ultimate bounded (UUB). Simulations are carried out to verify and clarify the theoretical findings.