Linear and Non-linear Multi-Input Multi-Output Model Predictive Control of Continuous Stirred Tank Reactor
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Abstract
In this article, multi-input multi-output (MIMO) linear model predictive controller (LMPC) based on state space model and nonlinear model predictive controller based on neural network (NNMPC) are applied on a continuous stirred tank reactor (CSTR). The idea is to have a good control system that will be able to give optimal performance, reject high load disturbance, and track set point change. In order to study the performance of the two model predictive controllers, MIMO Proportional-Integral-Derivative controller (PID) strategy is used as benchmark. The LMPC, NNMPC, and PID strategies are used for controlling the residual concentration (CA) and reactor temperature (T). NNMPC control shows a superior performance over the LMPC and PID controllers by presenting a smaller overshoot and shorter settling time.
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References
Tao Z., “Model Predictive Control”, P. 109 -110, 2010.
Goodwin et. al., “Control System Design”, Prentice Hall, 2001.
Henson, M. A., “Nonlinear Model Predictive Control: current status and future directions” Computers and Chemical Engineering, 1998. DOI: https://doi.org/10.1016/S0098-1354(98)00260-9
Pearson R. K., “Selecting Nonlinear Model Structures for Computer Control: Review”, Journal of Process Control, vol. 13, 2003. DOI: https://doi.org/10.1016/S0959-1524(02)00022-7
Junghui C., Tien C. H., “Applying Neural Networks to on-line Updated PID Controllers for Nonlinear Process Control”, Journal of Process Control, vol. 14, 2004. DOI: https://doi.org/10.1016/S0959-1524(03)00039-8
Manfred M., “Model Predictive Control Toolbox For Use with MATLAB”, User’s Guide, Ver.1, 1995.
Boo C. E., Hong M. K., Amy T. S., Khairiyah M. Y., “Formulation of Model Predictive Control Algorithm for Nonlinear Processes”, University of Technology Malaysia, 2006.
Qin, S. J., Bagwell, T. A., “An Overview of Industrial Predictive Control Technology”, Proceedings of 5th International Conference on chemical process control, 1997.
McKelvey T., Helmersson A. “State-Space Parameterizations of Multivariable Linear Systems using Tri-diagonal Matrix Forms”, CDC Kobe Japan, 1996.
Li S., Lim K.Y., Fisher D.G., “A State Space Formulation for Model Predictive Control”, AIChe Journal, Vol. 35, 1989. DOI: https://doi.org/10.1002/aic.690350208
Mark H. B., Martin T. H., Howard B. D., “Neural Network Toolbox 7 User’s Guide”, 2010.