INFERENTIAL MRAC NEURAL CONTROLLER FOR TEMPERATURE CONTROL OF CST PROCESS

摘要

This paper deals with model reference adaptive interferential controller (MRAC) using neural network, which has been proposed for the temperature control of CST process. The controller learns continuously, even while operating for control action so that the changes in the system are immediately reflected in control signal, and there is no need of explicit learning separately for dynamic adaptation. In this work, the feed-forward neural network has been used for the forward modeling of the plant. The network is trained using identification error that is the error between the plant output and output of the neural network model. The trained network parameters and tracking error have been used to construct the control law. The performance of the controller has been evaluated on the experimental setup of a continuously stirrer tank (CST) process. In the CST process, the controller has been used to control the temperature of water in the kettle by controlling the flow of coolant flowing in the jacket. The robust ness of the system has been confirmed for the set point tracking and also for under the influence of disturbances. The performance has been compared interms of integral square error (ISE) with the direct model reference neural adaptive controller.