Neural network model adaptation and its application to process control

Abstract A multi-layer perceptron network is made adaptive by weight updating using the extended Kalman filter (EKF). When the network is used as a model for a non-linear plant, the model can be on-line adapted with input/output data to capture system time-varying dynamics and consequently used in adaptive control. The paper describes how the EKF algorithm is used to update the network model and gives the implementation procedure. The developed adaptive model is evaluated for on-line modelling and model inversion control of a simulated continuous-stirred tank reactor. The modelling and control results show the effectiveness of model adaptation to system disturbance and a global tracking control.

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