ROBUST ADAPTIVE CONTROL USING REINFORCEMENT LEARNING FOR NONLINEAR SYSTEM WITH INPUT CONSTRAINTS

Abstract

This paper proposes a novel approach to design a controller in discrete time for the class of uncertain nonlinear systems in the presence of magnitude constrains of control signal which are treated as the saturation nonlinearity. A associative law between reinforcement learning algorithm based on adaptive NRBF neural networks and the theory of robust control Ho is set up in a novel control structure, in which the proposed controller allows learning and control on-line to compensate multiple uncertain nonlinearities as well as minimizing both the H. tracking performance index function and the unknown nonlinear dynamic approximation errors. The novel theorem of robust stabilization of the closed-loop system is declared and proved. Simulation results verify the theoretical analysis.