Skip to main content

Abstract

Proportional, integral and derivative controller tuning can be a complex problem. There are a significant number of tuning methods for this type of controllers. However, most of these methods are based on a single performance criterion, providing a unique solution representing a certain controller parameters combination. Thus, a broader perspective considering other possible optimal or near optimal solutions regarding alternative or complementary design criteria is not obtained. Tuning PID controllers is addressed in this paper as a many-objective optimization problem. A Multi-Objective Particle Swarm Optimization algorithm is deployed to tune PID controllers considering five design criteria optimized at the same time. Simulation results are presented for a set of four well known plants.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Åström, K.J., Hagglünd, T.: PID Controllers: Theory, Design, and Tuning, 2nd edn. The Instrument, Systems, and Automation Society, Research Triangle Park (1995)

    Google Scholar 

  2. Kano, M., Ogawa, M.: The state of the art in chemical process control in japan: Good practice and questionnaire survey. J. of Process Control 20(9) (2010)

    Google Scholar 

  3. Åström, K.J., Hagglünd, T.: Automatic Tuning of PID Controllers. Instrument Society of America, Research Triangle Park (1988)

    Google Scholar 

  4. Åström, K.J., Hägglund, T.: Advanced PID Control. ISA - The Instrumentation, Systems, and Automation Society Research Triangle Park (2006)

    Google Scholar 

  5. O’Dwyer, A.: Handbook of PI and PID Controller Tuning Rules, 2nd edn. Imperial College Press (February 2006)

    Google Scholar 

  6. Herreros, A., Baeyens, E., Péran, J.R.: Design of PID-type controllers using multiobjective genetic algorithms. ISA Trans 41(4), 457–472 (2002)

    Article  Google Scholar 

  7. Chiha, I., Liouane, N., Borne, P.: Tuning PID controller using multiobjective ant colony optimization. Appl. Comp. Intell. Soft. Comput. (January 2012)

    Google Scholar 

  8. Streeter, M.J., Keane, M.A., Koza, J.R.: Automatic synthesis using genetic programming of improved PID tuning rules. In: Ruano, A.E. (ed.) Preprints of the 2003 Intelligent Control Systems and Signal Processing Conf., Portugal, pp. 494–499.

    Google Scholar 

  9. Easter Selvan, S., Subramanian, S., Theban Solomon, S.: Novel Technique for PID Tuning by Particle Swarm Optimization. In: Seventh Annual Swarm Users/Researchers Conference, SwarmFest 2003 (2003)

    Google Scholar 

  10. Rajinikanth, V., Latha, K.: Tuning and retuning of PID controller unstable systems using evolutionary algorithm. Appl. Comp. Intell. Soft. Comput. (January 2012)

    Google Scholar 

  11. Abachizadeh, M., Yazdi, M.R.H., Yousefi-Koma, A.: Optimal tuning of PID controllers using Artificial Bee Colony algorithm. In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (2010)

    Google Scholar 

  12. Alcántara, S., Vilanova, R., Pedret, C., Skogestad, S.: A look into robustness/performance and servo/regulation issues in PI tuning. IFAC 2, 181–186 (2012)

    Google Scholar 

  13. Garpinger, O., Hägglund, T., Åström, K.J.: Criteria and trade-offs in PID design, Brescia, Italy (2012)

    Google Scholar 

  14. Reyes-Sierra, M., Coello, C.A.C.: Multi-objective particle swarm optimizers: A survey of the state-of-the-art. J. of Comp. Intelligence Research 2(3) (2006)

    Google Scholar 

  15. Fleming, P.J., Purshouse, R.C., Lygoe, R.J.: Many-Objective Optimization: An Engineering Design Perspective. Design 3410(6), 14–32 (2005)

    Google Scholar 

  16. Ishibuchi, H., Tsukamoto, N., Nojima, Y.: Evolutionary many-objective optimization: A short review. In: 2008 IEEE C. on Evolutionary Computation, pp. 2419–2426 (2008)

    Google Scholar 

  17. Coello, C.A.C., Lamont, G.B., Veldhuizen, D.A.V.: Evolutionary Algorithms for Solving Multi-Objective Problems (2002)

    Google Scholar 

  18. Adra, S., Fleming, P.: Diversity management in evolutionary many-objective optimization. IEEE Transactions on Evolutionary Computation 15(2), 183–195 (2011)

    Article  Google Scholar 

  19. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  20. Bansal, J.C.: et al.: Inertia weight strategies in particle swarm optimization. In: NaBIC, pp. 633–640. IEEE (2011)

    Google Scholar 

  21. Freire, H., de Moura Oliveira, P.B., Pires, E.J.S., Lopes, A.M.: MaxiMin MOPSO design of parallel robotic manipulators. In: Corchado, E., Snášel, V., Sedano, J., Hassanien, A.E., Calvo, J.L., Ślęzak, D. (eds.) SOCO 2011. Advances in Intelligent Systems and Computing, vol. 87, pp. 339–347. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  22. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Computation 6(1) (2002)

    Google Scholar 

  23. Skogestad, S., Postlethwaite, I.: Multivariable Feedback Control: Analysis and Design. John Wiley & Sons (2005)

    Google Scholar 

  24. Åström, K.J., Hägglund, T.: Benchmark systems for PID control. In: Digital Control – Past, present, and future of PID Control, Elsevier (2000)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Hélio F. Freire .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Freire, H.F., de Moura Oliveira, P.B., Solteiro Pires, E.J., Bessa, M. (2015). Many-Objective PSO PID Controller Tuning. In: Moreira, A., Matos, A., Veiga, G. (eds) CONTROLO’2014 – Proceedings of the 11th Portuguese Conference on Automatic Control. Lecture Notes in Electrical Engineering, vol 321. Springer, Cham. https://doi.org/10.1007/978-3-319-10380-8_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-10380-8_18

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10379-2

  • Online ISBN: 978-3-319-10380-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics