MULTIVARIABLE PROCESS-CONTROL USING DECENTRALIZED SINGLE NEURAL CONTROLLERS

This paper develops a learning-type multi-loop control system for inte racting multi-input/multi-output industrial process systems, The recen tly developed single neural controller (SNC) is adopted as the decentr alized controller, With a simple parameter tuning algorithm, the SNC i n each loop is able to learn to control a changing process by merely o bserving the process output error in the same loop, To circumvent loop interactions, static decouplers are incorporated in the presented sch eme, The only a priori knowledge of the controlled plant is the proces s steady state gains, which can be easily obtained from open-loop test , Extensive comparisons with decentralized PI controllers were perform ed, Simulation results show that the presented decentralized nonlinear control strategy appears to be a simple and promising approach to int eracting multivariable process control.