Multi-rate model predictive control of a chemical reactor based on three neural models

This paper describes a multi-rate model predictive control for a chemical reactor rig. Three multi-input single-output neural network models are developed with different sampling times; each for one reactor output. A multi-rate control strategy is proposed based on the multi-rate models, which enables reduced dimension optimisations to be used in majority of searching for optimal controls. In addition, some practical control techniques are also applied. Effectiveness of the proposed method is evaluated by both simulations and on-line control. A comparison with three decentralised PID controllers is presented.