DESIGN OF A NEURAL CONTROLLER BY INVERSE MODELING

This paper deals with the development a neural controller (a control system using a neural network) and its application for the temperature control of an experimental semi-batch pilot-plant reactor equipped with a monofluid heating-cooling system. The neural controller design methodology is based on the process inverse dynamics modelling : the learning data base is generated in an open-loop structure and the learning of the neural network is carried out by considering the future process outputs as the reference set-point. The first results presented deal with an ideal simulated system modelled by a first order system. They demonstrate the importance to take the time-delay of the plant into account. The second part is concerned with the real time application of such a technique to the temperature control of a semi-batch pilot-plant reactor and shows the real capability of the neural networks in process control.