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Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

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Robust Neural NARX Controller Design using Evolution and Learning

Published on September 2014 by P S Bhati, Rajeev Gupta
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Recent Advances in Wireless Communication and Artificial Intelligence
Foundation of Computer Science USA
RAWCAI - Number 1
September 2014
Authors: P S Bhati, Rajeev Gupta
2dbd716d-7f60-49a5-bc55-e0b790c438c4

P S Bhati, Rajeev Gupta . Robust Neural NARX Controller Design using Evolution and Learning. Recent Advances in Wireless Communication and Artificial Intelligence. RAWCAI, 1 (September 2014), 18-25.

@article{
author = { P S Bhati, Rajeev Gupta },
title = { Robust Neural NARX Controller Design using Evolution and Learning },
journal = { Recent Advances in Wireless Communication and Artificial Intelligence },
issue_date = { September 2014 },
volume = { RAWCAI },
number = { 1 },
month = { September },
year = { 2014 },
issn = 0975-8887,
pages = { 18-25 },
numpages = 8,
url = { /proceedings/rawcai/number1/17913-1409/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 Recent Advances in Wireless Communication and Artificial Intelligence
%A P S Bhati
%A Rajeev Gupta
%T Robust Neural NARX Controller Design using Evolution and Learning
%J Recent Advances in Wireless Communication and Artificial Intelligence
%@ 0975-8887
%V RAWCAI
%N 1
%P 18-25
%D 2014
%I International Journal of Computer Applications
Abstract

A robust neural NARX controller design using evolution and learning is proposed in this paper. Parameters of neural NARX controller are encoded in individual member (chromosome) of GA population. Neural NARX controller learns stabilizing response at an operating point of plant as best fitness reaches large steady value during successive generations. Stable operating region of neural NARX controller is enlarged by incrementally allowing learn more operating points. Best neural NARX controller in last generation is saved as designed neural NARX controller. Proposed approach is validated on a cart pole nonlinear plant model. Optimal stabilizing response with designed neural NARX controller over wide range of operating points is obtained.

References
  1. A. G. Barto, R. S. Sutton, C. W. Anderson, "Neuron like adaptive elements that can solve difficult learning control problems," IEEE Trans. Syst. Man, Cybern. , vol. SMC-13, pp. 834-846, 1983.
  2. Takashi Hiyama and DEng, "Application of rule-based stabilizing controller to electrical power system", IEE Proc. , part C, vol. 136, No. 3, pp. 175-181, May. 1889.
  3. J. S Jang, "ANFIS: Adaptive-network-based fuzzy inference system" IEEE Trans. Syst. Man, Cybern. , vol. 23, pp. 665-685, May 1993.
  4. Fumio Matsumura, Tom Namerikawa, Kazuhiko Hagiwara, and Masayuki Fujita, "Application of Gain Scheduled H? Robust Controllers to a Magnetic Bearing," IEEE Trans. Control Syst. Technol. , vol. 4, no. 5, pp. 484–493, Sept. 1996.
  5. Sannella, M. J. 1994 Constraint Satisfaction and Debugging for Interactive User Interfaces. Doctoral Thesis. UMI Order Number: UMI Order No. GAX95-09398. , University of Washington.
  6. Kai-Yew Lum, Vincent T. Coppola, and Dennis S. Bernstein, "Adaptive Autocentering Control for an . Active Magnetic Bearing Supporting a Rotor with Unknown Mass Imbalance," IEEE Trans. Control Syst. Technol. , vol. 4, no. 5, pp. 587–592, Sept. 1996.
  7. Marcio S. de Queiroz and Darren M. Dawson, "Nonlinear Control of Active Magnetic Bearings : A Backstepping Approach," IEEE Trans. Control Syst. Technol. , vol. 4, no. 5, pp. 545–552, Sept. 1996.
  8. Ali Charara, Jerome De Miras, and Bernard Caron, "Nonlinear Control of a Magnetic Levitation System Without Premagnetization," IEEE Trans. Control Syst. Technol. , vol. 4, no. 5, pp. 513–523, Sept. 1996.
  9. Hong-Chan Chang and Mang-Hui Wang, "Neural network based self-organizing fuzzy controller for transient stability of multimachine power system", IEEE Trans. on energy conversion, vol. 10, No. 2, pp. 339-347, June 1995.
  10. Takashi Hiyama, Yoshiteru Ueki and Hiroaki Andou, "Integrated fuzzy logic generator controller for stability enhancement", IEEE Trans. on energy conversion, vol. 12, No. 4, pp. 400-405, Dec. 1997.
  11. D. E. Goldberg, "Genetic Algorithms in search optimization and Machine learning", Addition Wesley 1989.
  12. A. Homaifar and E. McCormick, "Simultaneous design of membership functions and rule set for fuzzy controllers using genetic algorithms," IEEE Trans. Fuzzy Systems, vol. 3, pp. 129-139, April 1995.
  13. M. H. Lim, S Rahardja, and B. H. Gwee, "A GA paradigm for learning fuzzy rules," Fuzzy Sets and Systems, vol. 82, pp. 177-186, 1996.
  14. M. A. Lee and H. Takaji, "Integrating design stages of fuzzy systems using genetic algorithms," in Proc. IEEE int conf. Fuzzy Systems, pp. 612-617, New York, 1993.
  15. C. T. Lin, C. F. Juang, and C. P. Li, "Temperature control with a neural fuzzy inference network," IEEE Trans. Syst. Man, Cybern. , vol. 29, pp. 440-459, 1999.
  16. H. R. Berenji and P. Khedkar, "Learning and tuning fuzzy logic controllers through reinforcement" IEEE Trans. Neural Networks, vol. 3, pp. 724-740, May1992.
  17. C. T. Lin and C. S. G. Lee, "Reinforcement structure/parameter learning for neural-network-based fuzzy logic control systems," IEEE Trans. Fuzzy Syst. , vol. 2,pp. 46-63, Feb. 1994.
  18. C. T. Lin and C. P. Jou, "GA- Based Fuzzy Reinforcement Learning for Control of a Magnetic Bearing System",. IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 30, No. 2, pp. 276-289, April 2000.
  19. C. F. Juang, J. Y. Lin, C. T. Lin, "Genetic Reinforcement Learning Through Symbiotic Evolution for Fuzzy Controller Design", IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 30, No. 2, pp. 290-301, April 2000.
  20. S. F. Su, and S. H. Hsieh, "Embedding Fuzzy Mechanisms and Knowledge in Box-Type Reinforcement Learning Controllers", IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 32, No. 5, pp. 645-653, Oct. 2002.
  21. W. M. V. Buijtenen, G. Schram, R. Babuska, and H. B. Verbruggen, "Adaptive Fuzzy Control of Satellite Attitude by Reinforcement Learning", IEEE Trans. Fuzzy Systems, vol. 6, No. 2, pp. 185-194, 1998.
  22. P. Tsiotras and B. C. Wilson, "Zero and low-bias control designs for active magnetic bearings," IEEE Trans. Control Syst. Technol. , vol. 11, no. 6, pp. 889–904, Nov. 2003.
  23. P. Tsiotras and M. Arcak, "Low-bias control of AMB subject to voltage saturation: State-feedback and observer designs," IEEE Trans. Control Syst. Technol. , vol. 13, no. 2, pp. 262–273, Mar. 2005.
  24. M. S. de Queiroz and S. Pradhananga, "Control of Magnetic Levitation Systems with Reduced Steady-State Power Losses" IEEE Trans. On Control Systems Technology, vol. 15, No. 6, pp. 1096-1102, Nov. 2006.
  25. L. B. Antonio do Bomfim, G. N. Taranato, and D M Falcao, "Simultaneous tuning of power system damping controllers using genetic algorithms", IEEE Trans. on power system, vol. 15, No. 1, pp. 163-169, Feb. 2000.
  26. A. B. Chammas and C. T. Leondes, "Pole assignment by piecewise constant output feedback", Int. Journal on Control, Vol. 29, pp. 31-38, 1979.
  27. H. Werner and K. Furuta, "Simultaneous stabilization based on output measurement", Kybernetika, Vol. 31, pp. 395-411, 1995.
  28. R. Gupta, B. Bandyopadhyay and A. M. Kulkarani, "Design of power system stabilizer for single machine system using robust fast output sampling feedback technique" Elsevier Science, Electric Power System research, pp 247-257, 2003.
  29. R. Gupta, B. Bandyopadhyay and A. M. Kulkarani, "Robust decentralized fast output sampling technique based power system stabilizer for multi-machine power system", International Journal of System Science, Vol. 36. , No. 5, pp 297-314, April 2005.
  30. V. Bandal and B. Bandyopadhyay, "Robust decentralized output feedback sliding mode control technique-based power system stabilizer for multi-machine power system", IET Control Theory Applications, I(5), pp. 1512-1522, 2007.
  31. M. R. Gonzalez, and O. P. Malik, "Power system stabilizer design using an online adaptive neuro-fuzzy controller with adaptive input link weights", IEEE Trans. on energy conversion, vol. 23, No. 3 pp. 914-922, Sept. 2008.
  32. R. Polikar, L. Udpa, S. S. Udpa, and V. Honavar, "Learn ++: An incremental learning algorithm for supervised neural networks," IEEE Trans. Systems, Man, and Cybernetics- Appl. Rev. , Part C: vol. 31, No. 4, pp. 497-508, Nov 2001.
  33. K. Yamauchi, N. Yamauchi, and N. Ishii, "Incremental learning methods with retrieving of interfered patterns," IEEE Trans. Neural Networks, vol. 10, no. 6 pp. 1351-1365, Nov. 1999.
  34. Jing Yang, Zhong -Wei Le, and Jian- Pei Zhang, "A training algorithm of incremental support vector machine with recombining method," Proc. of 4th int. conf. on Machine Learning and Cybernetics, Gaungzhou, pp. 4285-4288, 18-21 August 2005.
  35. Sheng-Uei Guan, and Fangming Zhu, "An Incremental Approach to Genetic Algorithms Based Classification," IEEE Trans. Systems, Man, and Cybernetics- Part B: Cybernetics, vol. 35, No. 2, pp. 227-239, April 2005.
  36. H. Y. Quek, K. C. Tan, C. K. Goh, H. A. Abbass, "Evolution and incremental learning in iterated prisoner's dilemma", IEEE Trans. on Evolutionary Computation, vol. 13, No. 2, pp. 303-320, April 2009.
  37. X. Chen, Y. S. Ong, M. H. Lim, and K. C. Tan, "A multi-facet survey on memetic computation", IEEE Trans. on Evolutionary Computation, vol. 15, No. 5, pp. 591-607, Oct 2011.
  38. G. Acampora, J. M. Cadenas, V. Loia, and E. M. Ballester, "Achieving memetic adaptability by means of agent-based machine learning", IEEE Trans. on Evolutionary Computation, vol. 07, No. 4, pp. 557-569, Nov. 2011.
  39. S. S. Choi and B. R. moon, "A graph based Lamarckian-Baldwinian hybrid for the sorting network problem", IEEE Trans. on Evolutionary Computation, vol. 09, No. 1, pp. 105-114, Feb. 2005.
  40. Y. S. Ong, and A. J. Keane, "Meta-Lamarckian learning in memetic algorithms", IEEE Trans. on Evolutionary Computation, vol. 08, No. 2, pp. 099-110, April 2004.
  41. R. P. Prado, S. G. Galan, J. E. M. Exposito and A. J. Yuste, "Knowledge acquisition in fuzzy-rule-based systems with particle swarm optimization", IEEE Trans. On fuzzy systems vol. 18, No. 6, pp. 1083-1097, Dec. 2010.
  42. A. Abbadi, L. Nezli, D. Boukhetala, "A nonlinear voltage controller based on interval type 2 fuzzy logic control system for multi-machine power systems", International Journal of Electrical Power & Energy Systems, Volume 45, Issue 1, Pages 456–467, February 2013.
  43. P. S. Bhati, Rajeev Gupta, "Robust fuzzy logic power system stabilizer based on evolution and learning" International Journal of Electrical Power & Energy Systems, Volume 53, Issue 1, Pages 357-366, May 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Non-linear Auto Regressive With Exogenous Input (narx) Reinforcement (rf) Signal Performance Index (pi) Genetic Algorithms (gas).