Neural Network Predictive Control of a Chemical Reactor
Institute of Information Engineering, Automation and Mathematics, Faculty of Chemical and Food Technology, Slovak University of Technology, Radlinského 9, 81237 Bratislava, Slovakia
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Abstract: Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimize the future behaviour of a plant. MPC technology can now be found in a wide variety of application areas. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The controller 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.
Keywords: model predictive control, fuzzy control, PID control, neural network, continuous stirred tank reactor
Full paper in Portable Document Format: acs_0044.pdf
Acta Chimica Slovaca, Vol. 2, No. 2, 2009, pp. 21—36