Skip to main content
SpringerLink
Log in

Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing

  • Chapter
Intelligent Computational Optimization in Engineering

Part of the book series: Studies in Computational Intelligence ((SCI,volume 366))

Abstract

This chapter discusses theoretical and design aspects for optimal control systems with a reduced parametric sensitivity using Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. Sensitivity models with respect to the parametric variations of the controlled process are derived and the optimal control problems are defined. The new objective functions in these optimization problems are integral quadratic performance indices that depend on the control error and squared output sensitivity functions. Different dynamic regimes are considered. Relatively simple PSO and SA optimization algorithms are developed for the minimization of the objective functions, which optimize the control system responses and reduce the sensitivity to parametric variations of the controlled process. Examples of optimization problems encountered in the design of optimal proportional-integral (PI) controllers for a class of second-order processes with integral component are used to validate the proposed methods.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
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. Rosenwasser, E., Yusupov, R.: Sensitivity of automatic control systems. CRC Press, Boca Raton (2000)

    MATH  Google Scholar 

  2. Yaniv, O.: Design of low-order controllers satisfying sensitivity constraints for unstructured uncertain plants. Int. J. Robust Nonlinear Control 14, 1359–1370 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  3. Oded, T., Arkady, L.: Design of low order controllers satisfying sensitivity constraints for unstructured uncertain plants. In: Proceedings of 23rd IEEE Convention of Electrical and Electronics Engineers in Israel, Herzlia, Israel, pp. 33–36 (2004)

    Google Scholar 

  4. Shirao, J., Imai, J., Konishi, M.: Structure design with sensitivity control performance limitation for electromechanical systems. In: Proceedings of SICE-ICASE International Joint Conference SICE-ICCAS 2006, Busan, Korea, pp. 2362–2367 (2006)

    Google Scholar 

  5. Precup, R.E., Preitl, S., Korondi, P.: Fuzzy controllers with maximum sensitivity for servosystems. IEEE Trans. Ind. Electron 54, 1298–1310 (2007)

    Article  Google Scholar 

  6. Marchetti, G., Barolo, M., Jovanovic, L., Zisser, H., Seborg, D.E.: An improved PID switching control strategy for type 1 diabetes. IEEE Trans. Biomed. Eng. 55, 857–865 (2008)

    Article  Google Scholar 

  7. Wang, Y.G., Xu, X.M.: PID tuning for unstable processes with sensitivity specification. In: Proceedings of Chinese Control and Decision Conference CCDC 2009, Guilin, China, pp. 3460–3464 (2009)

    Google Scholar 

  8. Precup, R.E., Preitl, S.: Optimisation criteria in development of fuzzy controllers with dynamics. Eng. Appl. Artif. Intell. 17, 661–674 (2004)

    Article  Google Scholar 

  9. Köppen, M.: Light-weight evolutionary computation for complex image-processing applications. In: Proceedings of 6th International Conference on Hybrid Intelligent Systems HIS 2006, Auckland, New Zealand, pp. 3–3 (2006)

    Google Scholar 

  10. Zhou, H., Schaefer, G., Shi, C.: A mean shift based fuzzy c-means algorithm for image segmentation. In: Proceedings of 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society EMBC 2008, Vancouver, BC, Canada, pp. 3091–3094 (2008)

    Google Scholar 

  11. Schaefer, G., Nakashima, T., Zavisek, M.: Analysis of breast thermograms based on statistical image features and hybrid fuzzy classification. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Remagnino, P., Porikli, F., Peters, J., Klosowski, J., Arns, L., Chun, Y.K., Rhyne, T.-M., Monroe, L. (eds.) ISVC 2008, Part I. LNCS, vol. 5358, pp. 753–762. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  12. Abraham, A., Corchado, E., Corchado, J.M.: Hybrid learning machines. Neurocomputing 72, 2729–2730 (2009)

    Article  Google Scholar 

  13. Precup, R.E., Preitl, S.: On the stability and sensitivity analysis of fuzzy control systems for servo-systems. In: Nedjah, N., de Macedo Mourelle, L. (eds.) Fuzzy Systems Engineering, Theory and Practice, pp. 131–161. Springer, Heidelberg (2005)

    Google Scholar 

  14. Ekel, P.Y., Menezes, M., Schuffner Neto, F.H.: Decision making in a fuzzy environment and its application to multicriteria power engineering problems. Nonlinear Analysis: Hybrid Syst. 1, 527–536 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  15. Xu, J., Wu, H., Wang, Y.: Unpower aerocraft augmented state feedback tracking guaranteed cost control. J. Syst. Eng. Electron 19, 125–130 (2008)

    MATH  Google Scholar 

  16. Fmmo, C., Calado, JMF.: Approaches to human arm movement control - A review. Annu Rev Control 33, 69–77 (2009)

    Article  Google Scholar 

  17. Chen, J., Kong, C.K.: Performance assessment for iterative learning control of batch units. J. Process Control 19, 1043–1053 (2009)

    Article  Google Scholar 

  18. Kim, D.H., Abraham, A., Cho, J.H.: A hybrid genetic algorithm and bacterial foraging approach for global optimization. Inf. Sci. 177, 3918–3937 (2007)

    Article  Google Scholar 

  19. Nolle, L.: On a novel ACO-estimator and its application to the target motion analysis problem. Knowl-Based Syst. 21, 225–231 (2008)

    Article  Google Scholar 

  20. Köppen, M., Kinoshita, Y., Yoshida, K.: Auxiliary objectives for the evolutionary multi-objective principal color extraction from logo images. In: Proceedings of IEEE Congress on Evolutionary Computation CEC 2008, Hong Kong, China, pp. 3537–3544 (2008)

    Google Scholar 

  21. Plant, W.R., Schaefer, G., Nakashima, T.: An overview of genetic algorithms in simulation soccer. In: Proceedings of IEEE Congress on Evolutionary Computation CEC 2008, Hong Kong, China, pp. 3897–3904 (2008)

    Google Scholar 

  22. Kennedy, J., Eberhart, R.C.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks ICNN 1995, Perth, Australia, pp. 1942–1948 (1995)

    Google Scholar 

  23. Kennedy, J., Eberhart, R.C.: A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science MHS 1995, Nagoya, Japan, pp. 39–43 (1995)

    Google Scholar 

  24. Kirkpatrick, S., Gelatt Jr., C.D., Vecchi, M.P.: Optimization by simulated annealing. Science 20, 671–680 (1983)

    Article  MathSciNet  Google Scholar 

  25. Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution and the Bayesian restoration in images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)

    Article  MATH  Google Scholar 

  26. Kalai, A.T., Vempala, S.: Simulated annealing for convex optimization. Math. Oper. Res. 31, 253–266 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  27. Ledesma, S., Torres, M., Hernández, D., Aviña, G., García, G.: Temperature cycling on simulated annealing for neural network learning. In: Gelbukh, A., Kuri Morales, Á.F. (eds.) MICAI 2007. LNCS (LNAI), vol. 4827, pp. 161–171. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  28. David, R.C., Rădac, M.B., Preitl, S., Tar, J.K.: Particle swarm optimization-based design of control systems with reduced sensitivity. In: Proceedings of 5th International Symposium on Applied Computational Intelligence and Informatics SACI 2009, Timisoara, Romania, pp. 491–496 (2009)

    Google Scholar 

  29. Kim, T.H., Maruta, I., Sugie, T.: Robust PID controller tuning based on the constrained particle swarm optimization. Automatica 44, 1104–1110 (2008)

    Article  MathSciNet  Google Scholar 

  30. Lin, C.M., Lin, M.H., Chen, C.H., Yeung, D.S.: Robust PID control system design for chaotic systems using particle swarm optimization algorithm. In: Proceedings of 2009 International Conference on Machine Learning and Cybernetics ICMLC, Baoding, China, vol. 6, pp. 3294–3299 (2009)

    Google Scholar 

  31. Fang, H., Chen, L.: Application of an enhanced PSO algorithm to optimal tuning of PID gains. In: Proceedings of Chinese Control and Decision Conference CCDC 2009, Guilin, China, pp. 35–39 (2009)

    Google Scholar 

  32. Su, C., Wu, Y.: Adaptive neural network predictive control based on PSO algorithm. In: Proceedings of Chinese Control and Decision Conference CCDC 2009, Guilin, China, pp. 5829–5833 (2009)

    Google Scholar 

  33. Mukherjee, V., Ghoshal, S.P.: Intelligent particle swarm optimized fuzzy PID controller for AVR system. Electr. Power Syst. Res. 77, 1689–1698 (2007)

    Article  Google Scholar 

  34. Mohan Rao, A.R., Sivasubramanian, K.: Multi-objective optimal design of fuzzy logic controller using a self configurable swarm intelligence algorithm. Comput. Struct. 86, 2141–2154 (2008)

    Article  Google Scholar 

  35. Tang, H.Y., Ding, B., Qi, W.G.: Research on traffic mode of elevator applied fuzzy C-mean clustering algorithm based on PSO. In: Proceedings of International Conference on Measuring Technology and Mechatronics Automation ICMTMA 2009, Zhangjiajie, China, vol. 2, pp. 582–585 (2009)

    Google Scholar 

  36. Veenhuis, C., Köppen, M., Vicente-Garcia, R.: Evolutionary multi-objective optimization of particle swarm optimizers. In: Proceedings of IEEE Congress on Evolutionary Computation CEC 2007, Singapore, pp. 2273–2280 (2007)

    Google Scholar 

  37. Das, S., Abraham, A., Konar, A.: Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recognit. Lett. 29, 688–699 (2008)

    Article  Google Scholar 

  38. Shayeghi, H., Shayanfar, H.A., Jalili, A.: Load frequency control strategies: a state-of-the-art survey for the researcher. Energy Convers Manag. 50, 344–353 (2009)

    Article  Google Scholar 

  39. Sabat, S.L., dos Santos Coelho, L., Abraham, A.: MESFET DC model parameter extraction using quantum particle swarm optimization. Microelectron Reliab. 49, 660–666 (2009)

    Article  Google Scholar 

  40. Liang, X.R., Fan, Y.K., Jiang, T.: Application of PSO algorithm to coordinated ramp control. In: Proceedings of 2009 International Conference on Machine Learning and Cybernetics ICMLC, Baoding, China, vol. 3, pp. 1712–1716 (2009)

    Google Scholar 

  41. Zhang, Y., Hu, Y.: On PID controllers based on simulated annealing algorithm. In: Proceedings of 27th Chinese Control Conference CCC 2008, Kunming, China, pp. 225–228 (2008)

    Google Scholar 

  42. Cao, X.R.: Stochastic learning and optimization - A sensitivity-based approach. Annu. Rev. Control 33, 11–24 (2009)

    Article  Google Scholar 

  43. Qiu, X.Z., Xu, Z.G., Zhang, L.M., Zhou, J.X., Si, F.Q.: Nonlinear predictive control on the load system of a thermal power unit based on AOSVR and SAPSO. In: Proceedings of Asia-Pacific Power and Energy Engineering Conference APPEEC 2009, Wuhan, China, p. 4 (2009)

    Google Scholar 

  44. Wu, M., Xu, C.H., She, J.H., Yokoyama, R.: Intelligent integrated optimization and control system for lead-zinc sintering process. Control Eng. Pract. 17, 280–290 (2009)

    Article  Google Scholar 

  45. Haber, R.E., Haber-Haber, R., Jiménez, A., Galán, R.: An optimal fuzzy control system in a network environment based on simulated annealing. An application to a drilling process, Appl. Soft Comput. 9, 889–895 (2009)

    Google Scholar 

  46. Buyamin, S., Finch, J.W.: Comparative study on optimising the EKF for speed estimation of an induction motor using simulated annealing and genetic algorithm. In: Proceedings of IEEE International Electric Machines & Drives Conference IEMDC 2007, Antalya, Turkey, vol. 2, pp. 1689–1694 (2007)

    Google Scholar 

  47. Sayol, J., Nolle, L., Schaefer, G., Nakashima, T.: Comparison of simulated annealing and SASS for parameter estimation of biochemical networks. In: Proceedings of IEEE Congress on Evolutionary Computation CEC 2008, Hong Kong, China, pp. 3568–3571 (2008)

    Google Scholar 

  48. Qin, X.S., Huang, G.H., He, L.: Simulation and optimization technologies for petroleum waste management and remediation process control. J. Environ. Manag. 90, 54–76 (2009)

    Article  Google Scholar 

  49. Åström, K.J., Hägglund, T.: PID controllers theory: design and tuning. Instrument Society of America, Research Triangle Park, NC (1995)

    Google Scholar 

  50. Preitl, S., Precup, R.E.: An extension of tuning relations after symmetrical optimum method for PI and PID controllers. Automatica 35, 1731–1736 (1999)

    Article  MATH  Google Scholar 

  51. Abonyi, J.: Fuzzy model identification for control. Birkhäuser, Boston (2003)

    MATH  Google Scholar 

  52. Itagaki, N., Nishimura, H., Takagi, K.: Two-degree-of-freedom control system design in consideration of actuator saturation. IEEE/ASME Trans. Mechatronics 13, 470–475 (2008)

    Article  Google Scholar 

  53. Precup, R.E., Preitl, S.: PI and PID controllers tuning for integral-type servo systems to ensure robust stability and controller robustness. Electrical Eng (Archiv für Elektrotechnik) 88, 149–156 (2006)

    Article  Google Scholar 

  54. del Valle, Y., Venayagamoorthy, G.K., Mohagheghi, S., Hernandez, J.C., Harley, R.G.: Particle swarm optimization: Basic concepts, variants and applications in power systems. IEEE Trans. Evol. Comput. 12, 171–195 (2008)

    Article  Google Scholar 

  55. Khanesar, M.A., Tavakoli, H., Teshnehlab, M., Shoorehdeli, M.A.: A novel binary particle swarm optimization. In: Proceedings of Mediterranean Conference on Control & Automation MED 2007, Athens, Greece, p. 6 (2007)

    Google Scholar 

  56. Kessler, C.: Das symmetrische Optimum. Teil I. Regelungstechnik 6, 395–400 (1955)

    Google Scholar 

  57. Kessler, C.: Das symmetrische Optimum. Teil II. Regelungstechnik 6, 432–436 (1955)

    Google Scholar 

  58. Horváth, L., Rudas, I.J.: Modeling and problem solving methods for engineers. Academic Press, Elsevier, Burlington, MA (2004)

    Google Scholar 

  59. Johanyák, Z.C., Kovács, S.: Sparse fuzzy system generation by rule base extension. In: Proceedings of 11th International Conference on Intelligent Engineering Systems INES 2007, Budapest, Hungary, pp. 99–104 (2007)

    Google Scholar 

  60. Vaščák, J.: Fuzzy cognitive maps in path planning. Acta Tech. Jaurinensis Ser. Intell. Comput. 1, 467–479 (2008)

    Google Scholar 

  61. Klančar, G., Matko, D., Blažič, S.: Wheeled mobile robots control in a linear platoon. J. Intell. Robotic Syst. 54, 709–731 (2009)

    Article  Google Scholar 

  62. Chiou, J.S., Liu, M.T.: Numerical simulation for fuzzy-PID controllers and helping EP reproduction with PSO hybrid algorithm. Simul Modell Pract. Theory 17, 1555–1565 (2009)

    Article  Google Scholar 

  63. Liu, K., Tan, Y., He, X.: An adaptive staged PSO based on particles’ search capabilities. In: Tan, Y., Shi, Y., Tan, K.C. (eds.) ICSI 2010. LNCS, vol. 6145, pp. 52–59. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Automation and Applied Informatics, “Politehnica” University of Timisoara, Bd. V. Parvan 2, RO-300223, Timisoara, Romania

    Radu-Emil Precup, Radu-Codruţ David & Stefan Preitl

  2. School of Information Technology and Engineering, University of Ottawa, 800 King Edward, Ottawa, ON, K1N 6N5, Canada

    Emil M. Petriu

  3. Institute of Intelligent Engineering Systems, Óbuda University, Bécsi út 96/B, H-1034, Budapest, Hungary

    József K. Tar

Authors
  1. Radu-Emil Precup
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Radu-Codruţ David
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Stefan Preitl
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Emil M. Petriu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. József K. Tar
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Dept. Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, 820-8502, Izuka-Shi, Fukuoka, Japan

    Mario Köppen

  2. School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, U.K.

    Gerald Schaefer

  3. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, P.O. Box 2259, 98071-2259, Auburn, Washington, USA

    Ajith Abraham

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Precup, RE., David, RC., Preitl, S., Petriu, E.M., Tar, J.K. (2011). Optimal Control Systems with Reduced Parametric Sensitivity Based on Particle Swarm Optimization and Simulated Annealing. In: Köppen, M., Schaefer, G., Abraham, A. (eds) Intelligent Computational Optimization in Engineering. Studies in Computational Intelligence, vol 366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21705-0_7

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-21705-0_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-21704-3

  • Online ISBN: 978-3-642-21705-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation