Similarities of Model Predictive Control and Constrained Direct Inverse

ABSTRACT

To reach an acceptable controller strategy and tuning it is important to state what is considered “good”. To do so one can set up a closed-loop specification or formulate an optimal control problem. It is an interesting question, if the two can be equivalent or not. In this article two controller strategies, model predictive control (MPC) and constrained direct inversion (CDI) are compared in controlling the model of a pilot-scale water heater. Simulation experiments show that the two methods are similar, if the manipulator movements are not punished much in MPC, and they act practically the same when a filtered reference signal is applied. Even if the same model is used, it is still important to choose tuning parameters appropriately to achieve similar results in both strategies. CDI uses an analytic approach, while MPC uses numeric optimization, thus CDI is more computationally efficient, and can be used either as a standalone controller or to supplement numeric optimization.

To reach an acceptable controller strategy and tuning it is important to state what is considered “good”. To do so one can set up a closed-loop specification or formulate an optimal control problem. It is an interesting question, if the two can be equivalent or not. In this article two controller strategies, model predictive control (MPC) and constrained direct inversion (CDI) are compared in controlling the model of a pilot-scale water heater. Simulation experiments show that the two methods are similar, if the manipulator movements are not punished much in MPC, and they act practically the same when a filtered reference signal is applied. Even if the same model is used, it is still important to choose tuning parameters appropriately to achieve similar results in both strategies. CDI uses an analytic approach, while MPC uses numeric optimization, thus CDI is more computationally efficient, and can be used either as a standalone controller or to supplement numeric optimization.

Cite this paper

nullL. Tóth, L. Nagy and F. Szeifert, "Similarities of Model Predictive Control and Constrained Direct Inverse,"*Intelligent Control and Automation*, Vol. 3 No. 3, 2012, pp. 278-283. doi: 10.4236/ica.2012.33032.

nullL. Tóth, L. Nagy and F. Szeifert, "Similarities of Model Predictive Control and Constrained Direct Inverse,"

References

[1] G. C. Goodwin, “Inverse Problems with Constraints,” Proceedings of the 15th IFAC World Congress, Barcelona, 21-26 July 2002.

[2] F. Szeifert, T. Chován and L. Nagy, “Control Structures Based on Constrained Inverses,” Hungarian Journal of Industrial Chemistry, Vol. 35, 2007, pp. 47-55.

[3] D. Chen and D. E: Seborg, “PI/PID Controller Design Based on Direct Synthesis and Disturbance Rejection,” Industrial & Engineering Chemistry Research, Vol. 41, 2002, pp. 4807-4822. doi:10.1021/ie010756m

[4] S. Skogestad, “Simple Analytic Rules for Model Reduction and PID Controller Tuning,” Journal of Process Control, Vol. 13, No. 4, 2003, pp. 291-309. doi:10.1016/S0959-1524(02)00062-8

[5] M. Morari and J. H. Lee, “Model Predictive Control: Past, Present and Future,” Computers and Chemical Engineering, Vol. 23, No. 4-5, 1999, pp. 667-682. doi:10.1016/S0098-1354(98)00301-9

[6] M. A. Hosen, M. A. Hussain and F. S. Mjalli, “Control of Polystyrene Batch Reactors Using Neural Network Based Model Predictive Control (NNMPC): An Experimental Investigation,” Control Engineering Practice, Vol. 19, No. 5, 2011, pp. 454-467. doi:10.1016/j.conengprac.2011.01.007

[7] L. R. Tóth, L. Nagy and F. Szeifert, “Nonlinear Inversion-Based Control of a Distributed Parameter Heating System,” Applied Thermal Engineering, Vol. 43, 2012, pp. 174-179. doi:10.1016/j.applthermaleng.2011.11.032

[8] M. Abbaszadeh, “Nonlinear Multiple Model Predictive Control of Solution Polymerization of Methyl Methacrylate,” Intelligent Control and Automation, Vol. 2 No. 3, 2011, pp. 226-232. doi:10.4236/ica.2011.23027

[1] G. C. Goodwin, “Inverse Problems with Constraints,” Proceedings of the 15th IFAC World Congress, Barcelona, 21-26 July 2002.

[2] F. Szeifert, T. Chován and L. Nagy, “Control Structures Based on Constrained Inverses,” Hungarian Journal of Industrial Chemistry, Vol. 35, 2007, pp. 47-55.

[3] D. Chen and D. E: Seborg, “PI/PID Controller Design Based on Direct Synthesis and Disturbance Rejection,” Industrial & Engineering Chemistry Research, Vol. 41, 2002, pp. 4807-4822. doi:10.1021/ie010756m

[4] S. Skogestad, “Simple Analytic Rules for Model Reduction and PID Controller Tuning,” Journal of Process Control, Vol. 13, No. 4, 2003, pp. 291-309. doi:10.1016/S0959-1524(02)00062-8

[5] M. Morari and J. H. Lee, “Model Predictive Control: Past, Present and Future,” Computers and Chemical Engineering, Vol. 23, No. 4-5, 1999, pp. 667-682. doi:10.1016/S0098-1354(98)00301-9

[6] M. A. Hosen, M. A. Hussain and F. S. Mjalli, “Control of Polystyrene Batch Reactors Using Neural Network Based Model Predictive Control (NNMPC): An Experimental Investigation,” Control Engineering Practice, Vol. 19, No. 5, 2011, pp. 454-467. doi:10.1016/j.conengprac.2011.01.007

[7] L. R. Tóth, L. Nagy and F. Szeifert, “Nonlinear Inversion-Based Control of a Distributed Parameter Heating System,” Applied Thermal Engineering, Vol. 43, 2012, pp. 174-179. doi:10.1016/j.applthermaleng.2011.11.032

[8] M. Abbaszadeh, “Nonlinear Multiple Model Predictive Control of Solution Polymerization of Methyl Methacrylate,” Intelligent Control and Automation, Vol. 2 No. 3, 2011, pp. 226-232. doi:10.4236/ica.2011.23027