A neural-network-based model reference PID-like controller for process control

Abstract

This paper presents a neural-network-based PID-like control strategy applicable to a class of nonlinear control problems commonly encountered in the process-control industry. An artificial neural network is used to provide compensation of the plant's nonlinear dynamics so that the overall closed-loop system can be described in terms of an equivalent error system. In the paper, the strategy is carefully described, and then evaluated and compared with an alternative control system design which uses conventional gain-scheduled PID controllers. The paper includes real-time experimental results in applying the proposed technique for level control of a coupled-tanks system.