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Quantum Inspired GA based Neural Control of Inverted Pendulum

by D.k. Chaturvedi, Tanveer Qamar, O. P. Malik
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 23
Year of Publication: 2015
Authors: D.k. Chaturvedi, Tanveer Qamar, O. P. Malik
10.5120/21869-5210

D.k. Chaturvedi, Tanveer Qamar, O. P. Malik . Quantum Inspired GA based Neural Control of Inverted Pendulum. International Journal of Computer Applications. 122, 23 ( July 2015), 46-52. DOI=10.5120/21869-5210

@article{ 10.5120/21869-5210,
author = { D.k. Chaturvedi, Tanveer Qamar, O. P. Malik },
title = { Quantum Inspired GA based Neural Control of Inverted Pendulum },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 23 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 46-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number23/21869-5210/ },
doi = { 10.5120/21869-5210 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:11:22.619554+05:30
%A D.k. Chaturvedi
%A Tanveer Qamar
%A O. P. Malik
%T Quantum Inspired GA based Neural Control of Inverted Pendulum
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 23
%P 46-52
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper deals with comparison of artificial neural network and quantum inspired evolutionary neural network control of an inverted pendulum. First, a properly tuned PID controller was utilized to stabilize the inverted pendulum to generate the training data. Secondly, a feed-forward neural network was trained on the basis of these data. Thirdly, a quantum genetic algorithm optimized neural network was developed. If a disturbance occurs in the system, the controllers counteract this disturbance and balance inverted pendulum. All these three schemes are tested and compared. The results establish that the quantum genetic algorithm neural controller has the best control action.

References
  1. Sprenger B. , Kucera L. and Mourad S. (1998), "Balancing of an inverted pendulumwith a SCARA robot", IEEE/ASME Trans. Mechatronics, vol. 3, no. 2, pp. 91–97.
  2. Shuuji K. and Kazuo T. (1996), "Experimental study of biped dynamic walking", IEEE Contr. Syst. Mag. , vol. 16, no. 1, pp. 13–20.
  3. Pathak K. , Franch J. and Agrawal S. A. (2005), "Velocity and position control of a wheeled inverted pendulum", IEEE Trans. On Robotics, vol. 21, no. 3,pp. 505–514.
  4. Hsu P. (1992), "Dynamics and control design project offers taste of real world", IEEE Contr. Syst. Mag. , vol. 12, no. 3, pp. 31–39.
  5. Lin S. and Tsai C. (2009), " Development of a Self-Balancing Human Transportation Vehicle for the Teaching of Feedback Control", IEEE Transactions on Education, vol. 52, no. 1, pp. 157-168.
  6. Elshafei A. L. (2004), "Output feedback control of a class of nonlinear systems using fuzzy systems," Proc. IEEE Int. Symp. Intel. Contr. , pp. 620–625.
  7. Yoo B. and Ham W. (1998), "Adaptive fuzzy sliding mode control of nonlinear system', IEEE Trans. Fuzzy Syst. , vol. 6, no. 2, pp. 315–321.
  8. Lhee C. -G. , Park J. -S. , Ahn H. -S. and Kim D. -H. (2001), "Sliding mode-like fuzzy logic control with self-tuning the dead zone parameter", IEEE Trans. Fuzzy Syst. , vol. 9, no. 2, pp. 343–349.
  9. Wang L. X. (1994), "Adaptive Fuzzy Systems and Control", Englewood Cliffs, NJ: Prentice-Hall.
  10. Wang C. H. , Liu H. L. and Lin T. C. (2002), "Direct adaptive fuzzy-neural control with state observer and supervisory controller for unknown nonlinear dynamical systems", IEEE Trans. Fuzzy Syst. , vol. 10, no. 1, pp. 39–49.
  11. Barto A. G. , Sutton R. S. , and Anderson C. W. (1983), "Neuron like adaptive elements that can solve difficult learning control problems", IEEE Transaction on System, Man & Cybernatics, vol. SMC-13, pp. 834–846.
  12. Anderson C. W. (1989),"Learning to Control an Inverted Pendulum Using Neural Networks", IEEE Control Systems Magazine, vo1. 9,no. 3, pp. 31-37.
  13. Ishida T. , Shiokawa N. , Nagado T. and Ganeko S. (1991), "Learning control of an inverted pendulum using a neural network", in Proc. IEEE Conf. Industiral Electronics, Control and Instrumentation, vol. 2, pp. 1401–1404.
  14. Wang G. J. and Miu D. K. (1990), "Un-supervising adaptive neural-network control", in Proc. IEEE Int. Conf. Neural Networks, vol. 3, pp. 421–428.
  15. Hao J. , Tan S. and Vandewalle J. (1993), "A rule-based neural controller for inverted pendulum system", in Proc. IEEE Int. Conf. Neural Networks,vol. 1, pp. 534–539.
  16. Williams V. and Matsuoka K. (1991), "Learning to balance the inverted pendulum using neural networks", in Proc. IEEE Int. Joint Conf. Neural Networks, vol. 1, pp. 214–219.
  17. Sekiguchi M. , Sugasaka T. and Nagata S. (1991), "Control of a multi variable system by a neural network", in Proc. IEEE Int. Conf. Robotics and Automation, vol. 3, pp. 2644–2649.
  18. Yamamoto T. , Hanada S. , Nakazono K. , Kinjo H. and Tamaki S. (1995), "Neuro-control of Inverted Pendulum Evolved by GA with b u g h Evaluation", The Japan Society of Mechanical Engineers(Chapter C), Vol. 11, pp 154-159.
  19. Ikeda N. , Saito A. and Kitamura S. (1995), "Learning Control for Stabilization of an Inverted Pendulum Using a Multi-layered Neural Network", The Institute of System, Control and Information Engineers, Vol. 3.
  20. Matura K. and Morinaga M. (1996), "Improvement of Method Erecting a Pole in Cart and Pole Fuzzy Control System", Japan Society for Fuzzy Theory and Systems," Vol. 8.
  21. Cao S. G. , Rees N. W. and Feng G. (2000), "H? control of uncertain fuzzy continuous-time systems", Fuzzy Sets and Systems, vol. 115, no. 2, pp. 171–190.
  22. Lee K. R. , Jeung E. and Park H. B. (2001), "Robust fuzzy H? control for uncertain nonlinear system via state feedback: an LMI approach", Fuzzy Sets and Systems, vol. 120, pp. 123–134.
  23. Liu X. D. and Zhang Q. L. (2003), "New approaches to H? controller designs based on fuzzy observers for T-S fuzzy systems via LMI", Automatica, no. 39, pp. 1571–1582.
  24. Robert D. , Sename O. and Simon D. (2010), "An H? LPV Design for Sampling Varying Controllers: Experimentation With a T-Inverted Pendulum", IEEE Transactions on Control Systems Technolgy, vol. 18, no. 3, pp. 741-749.
  25. Caturvedi D. K. , "Soft Computing: Techniques and its Applications in Electrical Engineering", Springer, 2008
  26. Hey T. , "Quantum computing: an introduction," Computing & Control Engineering Journal, Piscataway, NJ: IEEE Press, vol. 10, no. 3, pp. 105-112, Jun. 1999.
  27. Nielsen M. A. and Chuang I. L. , Quantum Computation and Quantum Information. Cambridge University Press, 2000.
  28. Pittenger A. O. , An Introduction to Quantum Computing Algorithms . Boston: Birkhauser, 2000.
  29. Narayanan A. , "Quantum computing for beginners," in Proceedings of the 1999 Congress on Evolutionary Computation, Piscataway, NJ: IEEE Press, vol. 3, pp. 2231-2238, Jul. 1999.
  30. Preskill J. , Lecture Notes for Physics 229: Quantum Information and Computation. Department of Physics, California Institute of Technology, Sep 1998.
Index Terms

Computer Science
Information Sciences

Keywords

Quantum GA ANN Inverted Pendulum Control Adaptive Control Nonlinear system control Neural Control

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