Development Of A Neural Network Model Based Controller For A Non-linear Process Application

Process control using a model based structure incorporating a neural network is examined by application to the control of a real pilot-scale process exhibiting non-linearities and typical disturbances. Initially, a methodology for identibing an accurate neural network process model from plant data is described and practical aspects of applying the techniques are discussed. It is shown that the approach leads to a neural network description of the process dynamics that is suficiently accurate to be used independently from the process, emulating the process response from only process input information. The main success of the approach is the use of a novel coding technique for representing data in the network. The network model is incorporated into a model predictive control structure and on-line results illustrate the improvements in control performance that can be achieved compared to conventional PI control. Additionally, an insight into the dynamics and stability of the neural control scheme is obtained in a novel application of linear system identification techniques.