Model-Free Adaptive Learning Control with Prescribed Performance of Nonlinear Systems

Abstract

This paper proposes the data-driven prescribed performance adaptive learning control (DPPALC) algorithm to accomplish the prescribed performance control for nonlinear systems, which can ensure that the tracking error converges to the prescribed region in the prescribed time. Considering that system model is unavailable, a novel model learning algorithm is developed to equivalently represent the original system with a data model, in which the dynamic linearization technique is used to achieve this transformation. Then, the DPPALC algorithm is developed by the means of the transformed data equation. Compared with the existing methods, the main superiority of the presented learning algorithm is that the tracking error converges to the prescribed region within a given time by only using the system data. Two simulations are used to exemplify the devised DPPALC algorithm.