Fuzzy approximation-based adaptive tracking control for high-order nonlinear systems with asymmetric full-state constraints

Abstract

This paper studies the adaptive tracking control for a class of constrained high-order nonlinear systems with uncertainties. First, a type of improved asymmetric log-type barrier Lyapunov functions (LBLFs) is constructed to handle the full-state constraints. Second, fuzzy systems are built to approximate the unknown functions in the control system. An adaptive controller is designed on the basis of adding a power integrator technique. Consequently, the designed controller can ensure that the constraints are not breached, while the tracking error approaches an arbitrarily small neighborhood of the origin. The designed controller’s performance can be guaranteed with the disappearance of the constraints. The computational burden of fuzzy approximation is mitigated by the adaptive gain-update law. A numerical example and a practical example are presented to verify the effectiveness of the proposed method.

Adaptive tracking control for a class of stochastic nonlinear systems with input delay using multidimensional Taylor network

“…In addition, these control schemes cannot be directly applied to systems with unknown nonlinear functions, so that adaptive (NN) or fuzzy control methods based on approximation were designed, in which (NN) or fuzzy logic systems were mainly introduced to model unknown nonlinear functions [32,35,36,[38][39][40][41][42][43][44][45][46]. At the same time, classical adaptive techniques and backstepping methods were employed to construct expected controllers to ensure that all closed-loop signals remain bounded, the tracking error converged to a small neighborhood of the origin.…”