Adaptive neurofuzzy control for a class of state-dependent nonlinear processes

This paper presents a neurofuzzy-based scheme for modelling and control of a class of nonlinear systems with an autoregressive-moving-average-like model (a generalized Takagi-Sugeno fuzzy model), whose parameters are unknown nonlinear functions of the input and output variables of states of the plant. An associative memory network is used to identify each nonlinear function. The controller is a feedback linearizing control law which can decouple the nonlinearity of the system. For the cases of adaptive and the fixed model parameters, detailed close-loop stability analysis is carried out. It is shown that the consequent closed-loop system is globally stable. The main assumptions placed on the system and model for stability are minimum phase and a limit on the modelling mismatch error of uncertainty. Simulation examples are given to illustrate the efficacy of the proposed approach.