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Design and Implementation of Controller using MPC Toolbox

Omkar P. Dahale, Sharad P. Jadhav Published in Advanced Computing

IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014
Year of Publication: 2014
© 2014 by IJAIS Journal
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  1. Omkar P Dahale and Sharad P Jadhav. Article: Design and Implementation of Controller using MPC Toolbox. IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014 ICWAC 2014(1):11-17, June 2014. BibTeX

    @article{key:article,
    	author = "Omkar P. Dahale and Sharad P. Jadhav",
    	title = "Article: Design and Implementation of Controller using MPC Toolbox",
    	journal = "IJAIS Proceedings on International Conference and workshop on Advanced Computing 2014",
    	year = 2014,
    	volume = "ICWAC 2014",
    	number = 1,
    	pages = "11-17",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"
    }
    

Abstract

Model Predictive Control Toolbox provides tools for systematically analyzing, designing, and tuning model predictive controllers. Design and Simulation of model predictive controllers using functions in MATLAB or blocks can be done in Simulink. The predictive model, control and prediction horizons, input and output constraints, and weights can also be modified. The toolbox enables us to diagnose issues that could lead to run-time failures and provides advice on changing weights and constraints to improve performance and robustness. By running different scenarios in linear and nonlinear simulations, the controller performance can be evaluated. Adjustments to controller performance can be made as it runs by tuning weights and varying constraints. For rapid prototyping and embedded system design, the toolbox supports C-code generation.

Reference

  1. Eduardo F. Camacho and Carlos Bordons. Model Predictive Control. Springer, 2007.
  2. J. M. Maciejowski. Predictive Control with Constraints. Pearson Education Limited, 2002.
  3. J. A. Rossiter. Model-Based Predictive Control, A Practical Approach. CRC Press LLC, 2005.
  4. Liuping Wang. Model Predictive Control System Design and Implementation Using MATLAB. Springer, 2009.
  5. B. Wayne Bequette. Process Control: Modeling Design and Simulation. Prentice Hall PTR, 2002.
  6. Mathworks. Matlab documentation.
  7. S. J. Qin and T. A. Badgwell, "A survey of industrial model predictive control technology," Control Engineering Practice, vol. 11, pp. 733–764, 2002.
  8. J. B. R. D Q Mayne and C. V. Rao, "Constrained model predictive control: Stability and optimality," Automatica, vol. 36, 2000.
  9. C. E. Garcia, D. M. Prett, and M. Morari, "Model predictive control: theory and practice," Automatica, vol. 25, 1989.
  10. J. B. Jorgensen, "Moving horizon estimation and control," 2005.
  11. C. Brosilow and B. Joseph, Techniques of Model-Based Control. Prentice Hall, 2002.
  12. W. H. Fleming and R. W. Rishel, "Deterministic and stochastic optimal control," Springer, 1975.
  13. B. L. Kleinman, "An easy way to stabilize a linear constant system. ," IEEE Transactions on Automatic Control, vol. 15, no. 12, p. 693, 1970.
  14. S. J. Qin and T. A. Badgwell, "An overview of industrial model predictive control technology. " Fifth International Conference on Chemical Process Control, pp. 232–256, 1997.

Keywords

Model Predictive Control (MPC), MPC Toolbox, Single Input Single Output (SISO), Single Input Multiple Outputs (SIMO), MATLAB, Simulink, MPC Controller.