Reinforcement Adaptive Fuzzy Control for a Class of Nonlinear Uncertain Systems

Abstract

The paper describes the application of reinforcement learning techniques to feedback control for a class of nonlinear systems using adaptive fuzzy system. The adaptive fuzzy system approximates the non linear characteristics of the plant on-line to achieve the desired tracking accuracy without any preliminary off-line learning or training phase. The reinforcement signal from a critic is used for finding proper fuzzy rules to approximate the non linear dynamics in the plant. The approximation of the nonlinear dynamics in the plant is performed on-line without knowing any knowledge of the nonlinear terms in the plant, whence comes the term Reinforcement Adaptive Learning (RAL). The fuzzy system based on the RAL algorithm is referred to as Reinforcement Adaptive Fuzzy (RAF) system. The RAL algorithm is derived from Lyapunov stability analysis, so that both tracking stability and error convergence can be guaranteed in the closed-loop system.