A tuning approach for constrained MPC: Nominal stability ensured and energy consumption optimized

In order to get satisfying performances using the Model Predictive Control (MPC), one must find the suitable values of its tuning parameters. Usually in literature, this task is achieved using empirical or heuristic methods. In this paper, an analytical tuning approach is presented. As an advantage, the original approach we propose can be applied online to controllable process. The issues of closed-loop stability and energy consumed are addressed in this paper. Finally, a performance comparison is made with existing methods to emphasize the effectiveness of our approach.

[1]  S. Joe Qin,et al.  A survey of industrial model predictive control technology , 2003 .

[2]  Darci Odloak,et al.  An infinite horizon model predictive control for stable and integrating processes , 2003, Comput. Chem. Eng..

[3]  Eduardo F. Camacho,et al.  A generalized predictive controller for a wide class of industrial processes , 1998, IEEE Trans. Control. Syst. Technol..

[4]  E. Camacho,et al.  Generalized Predictive Control , 2007 .

[5]  Peyman Bagheri,et al.  Tuning of generalized predictive controllers for first order plus dead time models based on ANOVA , 2015, 2015 23rd Iranian Conference on Electrical Engineering.

[6]  Robert S. Balog,et al.  Auto-tuning the cost function weight factors in a model predictive controller for a matrix converter VAR compensator , 2015, 2015 IEEE Energy Conversion Congress and Exposition (ECCE).

[7]  D. Cooper,et al.  A Tuning Strategy for Unconstrained SISO Model Predictive Control , 1997 .

[8]  Ankush C. Jahagirdar,et al.  Effect of tuning parameters on performance of first-order plus dead-time processes using Generalized Predictive Control , 2015, 2015 International Conference on Industrial Instrumentation and Control (ICIC).

[9]  David W. Clarke,et al.  Generalized predictive control - Part I. The basic algorithm , 1987, Autom..

[10]  Peyman Bagheri,et al.  Adaptive tuning of model predictive control based on analytical results , 2016, 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA).

[11]  E. Iglesias,et al.  Tuning equation ford dynamic matrix control in siso loops , 2006 .

[12]  C. S. Cox,et al.  A filtered tuning method for a GPC controller , 2010 .

[13]  Leonardo Trujillo,et al.  Systematic selection of tuning parameters for efficient predictive controllers using a multiobjective evolutionary algorithm , 2015, Appl. Soft Comput..

[14]  H. Benlaoukli,et al.  Méthodes géométriques pour la construction des ensembles invariants. Application à la faisabilité des lois de commande prédictive , 2009 .

[15]  Liuping Wang,et al.  Model Predictive Control System Design and Implementation Using MATLAB , 2009 .

[16]  M. Soroush,et al.  Model Predictive Control Tuning Methods: A Review , 2010 .

[17]  Nicolas Langlois,et al.  Indirect adaptive model predictive control supervised by fuzzy logic , 2011, 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2011).