Predictive control using recurrent neural networks for industrial processes

Abstract

The paper presents a design methodology for predictive control of industrial processes via recurrent neural networks (RNNs). A discrete‐time mathematical model using RNN is established and a multi‐step neural predictor is then constructed. With the predictor, a neural predictive control (NPC) law is developed from the generalized predictive performance criterion. Both the RNN model and the NPC controller are proven convergent based on Lyapunov stability theory. Two examples of a nonlinear process system and a physical variable‐frequency oil‐cooling machine are used to demonstrate the effectiveness of the proposed control method. Through the experimental results, the proposed method has been shown capable of giving satisfactory performance for industrial processes under setpoint changes, external disturbances and load changes.

Key words:

• generalized predictive control
• process control
• recurrent neural networks
• variable‐frequency oil‐cooling machine

Notes

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