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Multi-objective Performance Evaluation of Controllers for a Thermal Process

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 151)

Abstract

Most engineering systems are multivariable in nature, where more than one input controls more than one output. The challenge arises in controlling these types of systems due to interaction among inputs and outputs. In an attempt to optimise the performance of these processes, many performance objectives need to be considered simultaneously. In most cases, these objectives often conflict hence a need for Multi-objective Optimisation (MOO) analysis. In this paper MOO design for Model Predictive Control (MPC) and Proportional Integral (PI) control are investigated for a multivariable process. The Pareto sets for both controllers is generated using Pareto Differential Evolution (PDE) and then compared using n-dimensional visualization tool, Level Diagrams to evaluate which controller is best for the process. Solutions which provide a preferred performance are then selected and tested experimentally on a thermal process.

Notes

Acknowledgment

We would like to thank iThemba LABS for the financial support.

References

  1. 1.
    Gambier A (2003) MPC and PID control based on multi-objective optimization. In: American control conference, Washington, USA, 2008Google Scholar
  2. 2.
    Liu GP, Yang JB, Whidborne JF (2003) Multiobjective optimisation and control. Research Studies Press Ltd, HertfordshireGoogle Scholar
  3. 3.
    Tavakoli S, Banookh A (2010) Robust PI control design using particle swarm optimization. J Comp Sci Eng 1(1):36–41Google Scholar
  4. 4.
    Huang H, Riggs JB (2002) Comparison of PI and MPC for control of a gas recovery unit. J Process Control 12:163–173CrossRefGoogle Scholar
  5. 5.
    Ahmad A, Wahid A (2007) Application of model predictive control (MPC) tuning strategy in multivariable control of distillation column. Reaktor 11(2)66–70Google Scholar
  6. 6.
    Ali AM, Ajbar A, Alhumaizi K (2010) Robust model-based control of a tabular reverse-osmosis desalination unit. Desalination 255: 129–136CrossRefGoogle Scholar
  7. 7.
    Popov A, Farag A, Werner H (2005) Tuning of a PID controller using a multi-objective optimization technique applied to a neutralization plant. In: Proceedings of the 44th IEEE conference on decision and control, SevilleGoogle Scholar
  8. 8.
    Tavakoli S, Griffin I, Fleming PJ (2007) Multi-objective optimization approach to the PI tuning problem. Evolutionary Computation, pp 3156–3171Google Scholar
  9. 9.
    Gerulf K, Pedersen M, Yang Z (2006) Multi-objective PID-controller tuning for a magnetic levitation system using NSGA-II. In: Proceedings of the genetic and evolutionary computation conference, Seattle, pp 1737–1744Google Scholar
  10. 10.
    Yali X, Li D, Gao F (2010) Multi-objective optimization and selection for the PI control of ALSTOM gasifier problem. Control Eng Pract 18:67–76CrossRefGoogle Scholar
  11. 11.
    van der Lee JH, Svrcek WY, Young BR (2008) A tuning algorithm for model predictive controller based on genetic algorithms and fuzzy decision making. ISA Trans 47:53–59CrossRefGoogle Scholar
  12. 12.
    Vega P, Francisco M (2008) Multiobjective optimization for automatic tuning of robust model based predictive controllers. In: Proceedings of the 17th World Congress the international Federation of Automatic Control, SeoulGoogle Scholar
  13. 13.
    Zitzler E, Thiele L, Laumanns M, Fonseca CM, da Fonseca VG (2003) Performance assessment of multiobjective optimizers: an analysis and review. IEEE Trans Evol Comput 7(2)CrossRefGoogle Scholar
  14. 14.
    Blasco X, Herrero JM, Sanchis J, Martinez M (2008) A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Inform Sci 178:3908–3924MATHCrossRefGoogle Scholar
  15. 15.
    Kollat JB, Reed P (2007) A framework for visually interactive decision-making and design using evolutionary multi-objective optimization (video). Environ Modell Softw 22:1691–1704CrossRefGoogle Scholar
  16. 16.
    Gonzalez AH, Adam EJ, Marchetti JL (2008) Conditions for offset elimination in state space receding horizon controllers: a tutorial analysis. Chem Eng Process 47:2184–2194CrossRefGoogle Scholar
  17. 17.
    Wang L (2008) Model predictive control system design and implementation using MATLAB. Springer, New YorkGoogle Scholar
  18. 18.
    FG S (1994) Robust and adaptive control of an unknown plant: a benchmark of new format. Automatica 30(4):567–575Google Scholar
  19. 19.
    Moore D (2009) Optimal controller comparison using Pareto fronts. CISSEGoogle Scholar
  20. 20.
    Abbass HA, Sarker R (2002) The Pareto differential evolution algorithm. Artif Intell Tools. 11(4):531–552CrossRefGoogle Scholar
  21. 21.
    Abbass HA, Sarker R, Newton C (2001) PDE: a Pareto-frontier differential evolution approach for multi-objective optimization problems. University of New South WalesGoogle Scholar
  22. 22.
    Garriga JL, Soroush M (2010) Model predictive control tuning methods: a review. Ind Eng Chem Res 49:3505–3515CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Electrical EngineeringUniversity of Cape Town Private BagRondeboschSouth Africa

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