Neuro-predictive process control using online controller adaptation

This paper proposes a technique of integrating neural networks with conventional controller structures, for the predictive control of complex process systems. In the developed method, a baseline conventional controller, e.g. a PI controller, is used to control the process. In addition, a recurrent neural network is used in the form of a multi-step-ahead predictor (MSP) to model the process dynamics. Utilizing the MSP capabilities of recurrent neural networks, the parameters of the conventional controller can be tuned by a backpropagation-like approach, to achieve acceptable regulation and stabilization of the controlled process variables. The advantage of such a formulation is the effective online adaptation of the controller parameters while the process is in operation, and the tracking of the different operating regimes and variations in process characteristics. The developed method is applied for the stabilization and transient control of U-tube steam generator water level