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Particle Swarm Optimization-Based Extremum Seeking Control

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Advanced Intelligent Computing Theories and Applications (ICIC 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6215))

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Abstract

This paper devises a particle swarm optimization-based extremum seeking control (ESC) scheme. In the scheme, the system states are guided to the optimal point by the controller based on the output measurement, and the explicit form of the performance function is not needed. By measuring the performance function value online, a sequence, generated by the particle swarm optimization algorithm, steers the regulator that drives the system states approaching to the set point that optimizes the performance. We also propose an algorithm that first reshuffles the sequence, and then inserts intermediate states into the sequence, to reduce the regulator gain and oscillation induced by stochastic, population-based searching algorithms. Simulation examples demonstrate the effectiveness and robustness of the proposed scheme.

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References

  1. Bastin, G., Nesic, D., Tan, Y., Mareels, I.: On extremum seeking in bioprocesses with multivalued cost functions. Biotechnol. Prog. 25(3), 683–689 (2009)

    Article  Google Scholar 

  2. Beaudoin, J.F., Cadot, O., Aider, J.L., Wesfreid, J.E.: Drag reduction of a bluff body using adaptive control methods. Phys. Fluids 18(8) (August 2006)

    Google Scholar 

  3. Dasheng, L., Tan, K.C., Goh, C.K., Ho, W.K.: A multiobjective memetic algorithm based on particle swarm optimization. IEEE J. SMCB 37(1), 42–50 (2007)

    Google Scholar 

  4. DeHaan, D., Guay, M.: Extremum-seeking control of state-constrained nonlinear systems. Automatica 41(9), 1567–1574 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. El-Zonkoly, A.M.: Optimal tuning of power systems stabilizers and avr gains using particle swarm optimization. Expert Systems with Applications 31(3), 551–557 (2006)

    Article  Google Scholar 

  6. Guay, M., Zhang, T.: Adaptive extremum seeking control of nonlinear dynamic systems with parametric uncertainties. Automatica 39(7), 1283–1293 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  7. Hudon, N., Guay, M., Perrier, M., Dochain, D.: Adaptive extremum-seeking control of convection-reaction distributed reactor with limited actuation. Comput. Chem. Eng. 32(12), 2994–3001 (2008)

    Article  Google Scholar 

  8. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Neural Networks, pp. 1942–1948. IEEE Press, Proceedings (1995)

    Google Scholar 

  9. Killingsworth, N., Krstic, M.: Pid tuning using extremum seeking: online, modelfree performance optimization. IEEE Contr. Syst. Mag. 26(1), 70–79 (2006)

    Article  Google Scholar 

  10. Krstic, M.: Performance improvement and limitations in extremum seeking control. Systems & Control Letters 39(5), 313–326 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  11. Krstic, M., Wang, H.H.: Stability of extremum seeking feedback for general nonlinear dynamic systems. Automatica 36(4), 595–601 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  12. Laskari, E.C., Parsopoulos, K.E., Vrahatis, M.N.: Particle swarm optimization for minimax problems. In: Proceedings of the 2002 Congress on Evolutionary Computation CEC 2002, vol. 2, pp. 1576–1581 (2002)

    Google Scholar 

  13. Liang, J.J., Qin, A.K., Suganthan, P.N., Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions. IEEE T. Evolut. Comput. 10(3), 281–295 (2006)

    Article  Google Scholar 

  14. Panda, G., Mohanty, D., Majhi, B., Sahoo, G.: Identification of nonlinear systems using particle swarm optimization technique. In: IEEE Congress on Evolutionary Computation CEC 2007, pp. 3253–3257 (2007)

    Google Scholar 

  15. Rapaic, M.R., Kanovic, Z.: Time-varying pso - convergence analysis, convergencerelated parameterization and new parameter adjustment schemes. Information Processing Letters 109(11), 548–552 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  16. Kabadi, A.P. (ed.): The Bottleneck TSP. The Traveling Salesman Problem and Its Variation. Kluwer Acadamic Publishers, Netherlands (2002)

    Google Scholar 

  17. Xin, C., Yangmin, L.: A modified pso structure resulting in high exploration ability with convergence guaranteed. IEEE J. SMCB 37(5), 1271–1289 (2007)

    Google Scholar 

  18. Zhang, C., Ordonez, R.: Numerical optimization-based extremum seeking control with application to abs design. IEEE T. Automat. Contr. 52(3), 454–467 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  19. Zhang, C., Ordonez, R.: Robust and adaptive design of numerical optimizationbased extremum seeking control. Automatica 45(3), 634–646 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  20. Zhong, Z.d., Huo, H.b., Zhu, X.j., Cao, G.y., Ren, Y.: Adaptive maximum power point tracking control of fuel cell power plants. J. Power Sources 176(1), 259–269 (2008)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Electrical Engineering College, Wuhan University, Wuhan, 430074, China

    Shi-Jie Yu

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Hong Chen & Li Kong

  3. Wuhan 2nd Ship Design and Research Institute, Wuhan, 430064, China

    Hong Chen

Authors
  1. Shi-Jie Yu
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  2. Hong Chen
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  3. Li Kong
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Editor information

Editors and Affiliations

  1. Intelligent Computing Laboratory, Chinese Academy of Sciences, P.O. Box 1130, 230031, Hefei, Anhui, China

    De-Shuang Huang

  2. Department of Biomedical Informatics, Vanderbilt University Medical Center, 2,525 West End Avenue, Suite 600, 37203, Nashville, TN, USA

    Zhongming Zhao

  3. Electrical and Electronics Department, Polytechnic of Bari, Via Orabona 4, 70125, Bari, Italy

    Vitoantonio Bevilacqua

  4. Faculty of Engineering, District University Francisco José de Caldas, Cra. 7a,No. 40-53, Fifth Floor, Bogotá, Colombia

    Juan Carlos Figueroa

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© 2010 Springer-Verlag Berlin Heidelberg

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Yu, SJ., Chen, H., Kong, L. (2010). Particle Swarm Optimization-Based Extremum Seeking Control. In: Huang, DS., Zhao, Z., Bevilacqua, V., Figueroa, J.C. (eds) Advanced Intelligent Computing Theories and Applications. ICIC 2010. Lecture Notes in Computer Science, vol 6215. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14922-1_24

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  • DOI: https://doi.org/10.1007/978-3-642-14922-1_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14921-4

  • Online ISBN: 978-3-642-14922-1

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