A NONLINEAR PID-BASED MULTIPLE CONTROLLER INCORPORATING A MULTILAYERED NEURAL NETWORK LEARNING SUBMODEL

Abstract

A new nonlinear minimum-variance adaptive proportional integral derivative (PID) based multiple controller, incorporating a multi- layered neural network learning submodel, is presented. The unknown non-linear plant is represented by an equivalent stochastic model consisting of a linear least-squares-based submodel plus a non- linear multi-layered back propagation (BP) neural network-based learning submodel. The proposed multiple controller methodology provides the designer with a choice of using either a conventional PID self-tuning controller, a PID structure-based pole-placement controller, or a newly proposed PID structure-based pole-zero placement controller through simple switching. The novel PID structure based pole-zero placement controller employs an adaptive mechanism, which ensures that the closed-loop poles and zeros are located at their prespecified positions. The switching decision between the different nonlinear fixed structure controllers is made manually in the present case but can be automated using fuzzy logic or stochastic learning automata techniques. Simulation results using a nonlinear plant model demonstrate the effectiveness of the proposed multiple controller with respect to tracking set-point changes. The aim is to achieve a desired speed of response while penalizing excessive control action, for applications in nonminimum phase and unstable systems.