Optimal predictor and implicit self-tuning regulator for a class of Hammerstein large-scale systems

Abstract

This paper presents an optimal predictor and a self-tuning control scheme to solve the regulation problem of large-scale systems. We consider the class of large-scale nonlinear system which can be decomposed into single-input single-output interconnected nonlinear subsystems. Each interconnected subsystem can operate in a stochastic environment and described by discrete-time Hammerstein mathematical model, with unknown time-varying parameters. Self-tuning regulator algorithm for large-scale nonlinear stochastic systems is developed on the basis upon the minimum variance approach with implicit scheme. The performance of the proposed self-tuning regulator is evaluated by simulation example.