Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimi ze the future behaviour of a plant. MPC technology can now be found in a wide variety of ap plication areas. The neural network predictive controller that is discussed in this pap er uses a neural network model of a nonlinear plant to predict future plant performance. The cont roller calculates the control input that will optimize plant performance over a specified future time horizon. In the paper, simulation of the neural network based predictive control of the continuous stirred tank reactor is presented. The simulation results are compared with fuzzy and PID control.
['Anna Vasičkaninová', 'Monika Bakošová']
Neural Network Predictive Control Of A Chemical Reactor.