Min-max predictive control of a heat exchanger using a neural network solver

• Published in: IEEE transactions on control systems technology Vol. 12; no. 5; pp. 776 - 786
• Main Authors: Ramirez, D.R., Arahal, M.R., Camacho, E.F.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.09.2004; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Applied sciences; Computer science; control theory; systems; Control system synthesis; Control systems; Control theory. Systems; Delay effects; Exact sciences and technology; Miscellaneous; Neural networks; Predictive control; Predictive models; Process control; Process control. Computer integrated manufacturing; Robust control; Sampling methods; Uncertain systems; Uncertainty; Uncertain system; Process control; Heat exchanger; Minimax method; Delay time; Neural network; Sampling; Delayed time; Predictive control
• ISSN: 1063-6536; 1558-0865
• DOI: 10.1109/TCST.2004.826972

Min-max model predictive controllers (MMMPC) have been proposed for the control of linear plants subject to bounded uncertainties. The implementation of MMMPC suffers a large computational burden due to the numerical optimization problem that has to be solved at every sampling time. This fact severely limits the class of processes in which this control is suitable. In this brief, the use of a neural network (NN) to approximate the solution of the min-max problem is proposed. The number of inputs of the NN is determined by the order and time delay of the model together with the control horizon. For large time delays the number of inputs can be prohibitive. A modification to the basic formulation is proposed in order to avoid this latter problem. Simulation and experimental results are given using a heat exchanger.