PROCESS-CONTROL BY ONLINE TRAINED NEURAL CONTROLLERS

Although neural controllers based on multilayer neural networks have been demonstrating high potential in the nonconventional branch of adaptive process control called neurocontrol, practical applications are severely limited by the long training time that they require. This paper addresses the question of how to perform on-line training of multilayer neural controllers in an efficient way in order to reduce the training time. At first, based on multilayer neural networks, structures for a plant emulator and a controller are described. Only a little qualitative knowledge about the process to be controlled is required. The controller must learn the inverse dynamics of the plant from randomly chosen initial weights. Basic control configurations are briefly presented and new on-line training methods, based on performing multiple updating operations during each sampling period, are proposed and described in algorithmic form. One method, the direct inverse control error approach, is effective for small adjustments of the neural controller when it is already reasonably trained; another, the predicted output error approach, directly minimizes the control error and greatly improves convergence of the controller. Simulation and experimental results using a simple plant show the effectiveness of the proposed neuromorphic control structures and training methods.