Prescribed‐Time Adaptive Dynamic Surface Control for Non‐Strict‐Feedback Nonlinear Systems
TL;DR
This paper investigates the practical prescribed‐time (PPT) tracking control problem for a class of nonlinear non‐strict‐feedback systems with arbitrarily bounded initial states with arbitrarily bounded initial states and employs a radial basis function neural network to approximate unknown and possibly non‐differentiable system dynamics.
Abstract
In this paper, we investigate the practical prescribed‐time (PPT) tracking control problem for a class of nonlinear non‐strict‐feedback systems with arbitrarily bounded initial states. A radial basis function neural network (RBFNN) is employed to approximate unknown and possibly non‐differentiable system dynamics. By introducing a preset error trajectory, the tracking error is guided along a predefined evolution, it not only avoids the singularities and parameter‐coupling issues commonly associated with time‐varying mappings but also allows arbitrarily bounded initial errors. A Lyapunov‐like function is further constructed to guarantee closed‐loop stability while implicitly accommodating the filtering errors generated by the dynamic surface control (DSC) and maintaining bounded transient behavior. Based on Lyapunov analysis, all closed‐loop signals are shown to be uniformly bounded. The effectiveness of the proposed method is demonstrated through two illustrative examples.