This paper deals with the self-tuning regulator for large-scale stochastic nonlinear systems, which are composed of several interconnected nonlinear monovariable subsystems. Each interconnected subsystem is described by discrete Hammerstein model with unknown and time-varying parameters. This self-tuning control is developed on the basis of the minimum variance approach and is combined by a recursive algorithm in the estimation step. The parametric estimation step is performed on the basis of the prediction error method and the least-squares techniques. Simulation results of the proposed self-tuning regulator for two interconnected nonlinear hydraulic systems show the reliability and effectiveness of the developed method.
Wiener Filter Based Minimum Variance Self-Tuning Regulation
