On Multivariable Neural Network Decoupling Control System

Abstract

Based on the principle of decoupling and neural-network, this paper extends the single-loop single neural control system to multivariable case of the temperature-liquid level two-variable interacting control system in the front box of the pressure net of the papermaking machine. By incorporating static feed-forward decoupling compensation, a learning-type decentralize multivariable control system has been proposed. With a parameter tuning algorithm, the nonlinear single neural controller (SNC) in each loop is able to control a changing process by merely observing the process output error in the loop. The only a priori plant information is the process steady state gain, which can be easily obtained from open-loop test. Thus, good regulating performance is guaranteed in the initial control stage, even the controlled object varies later. Simulation results show that this strategy is effective and practicable