Genetic Algorithm On Line Controller for the Flexible Inverted Pendulum Problem

Elmer P. Dadios; Patrick S. Fern; ez; David J. Williams

doi:10.20965/jaciii.2006.p0155

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JACIII Vol.10 No.2 pp. 155-160

doi: 10.20965/jaciii.2006.p0155

(2006)

Paper:

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Genetic Algorithm On Line Controller for the Flexible Inverted Pendulum Problem

Elmer P. Dadios^, Patrick S. Fernandez^, and David J. Williams^

^*Department of Manufacturing Engineering and Management, 2401 Taft Avenue, De La Salle University, Manila 1004, Philippines

^**National Power Corporation, Diliman, Quezon City 1004, Philippines

^***Loughborough University, Loughborough Leicestershire, LE11 3TU, U.K.

Received:

January 8, 2005

Accepted:

August 25, 2005

Published:

March 20, 2006

Keywords:

Genetic Algorithm (GA), learning controllers, flexible inverted pendulum problem

Abstract

This paper presents a real time controller for a highly non-linear system. The Flexible Pole-Cart Balancing Problem (FPCBP) is used as the test case to investigate the learning capability of Genetic Algorithm (GA) in physical application. The controller developed is initially trained using a set of data taken from on line dynamics of the flexible pole cart balancing system. Based from the physical data of the system, the weights W1 to W6 are optimized by the genetic algorithm in order to determine the correct value of the force applied to the cart. The trained GA-based controller then controls the physical Flexible Pole-Cart Balancing system for infinite time. Analysis on the behavior of the GA model developed is presented. Results of the physical experiments show that the controller developed is accurate, adaptive and robust.

Cite this article as:

E. Dadios, P. Fernandez, and D. Williams, “Genetic Algorithm On Line Controller for the Flexible Inverted Pendulum Problem,” J. Adv. Comput. Intell. Intell. Inform., Vol.10 No.2, pp. 155-160, 2006.

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References

[1] F. O. Karray, and C. De Silva, “Soft Computing and Intelligent Systems Design,” PEARSON Addison Wesley, Edenburgh Gate, Harlow, Essex CM20 2JE, England, 2004.
[2] D. Dasgupta, and Z. Michalewics, “Evolutionary Algorithms in Engineering Applications,” Springer-Verlag, Berlin Heidelberg, 1997.
[3] J. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, 1992.
[4] W. Langdon, “GP + Data Structure = Automatic Programming,” Kluwer Academic Publisher, New Your, 1994.
[5] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Reading, MA: Addison-Wesley, 1989.
[6] P. Ross, and D. Corne, “Applications of Genetic Algorithms,” Prentice Hall PTR, Englewood Cliffs, 1995.
[7] J. H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor, MI: Univ. Michigan Press, 1975.
[8] S. R. Ladd, “Genetic Algortihms in C++,” MIT Books, New York, 1997.
[9] W. Wienholt, “A Refined Genetic Algorithm for Parameter Optimization Problems,” Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 589-596, Morgan Kaufmann Publishers, 1993.
[10] Z. Michalewicz, and M. Michalewicz, “Evolution Computation Techniques and their Application,” in IEEE International Conference on Intelligent Processing Systems, pp. 14-26, 1997.
[11] D. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Transactions on Neural Networks, Vol.5, No.1, pp. 3-14, 1994.
[12] T. Hatanaka, K. Uosaki, H. Tanaka, and Y. Yamada, “Systems Parameter Estimation by Evolutionary Strategy,” Proceedings of the 35^th SICE Annual Conference, pp. 1045-1048, 1996.
[13] G. W. Greenwood, A. Gufta, and K. Mcsweeney, “Scheduling Task in Multiprocessor Systems Using Evolutionary Strategies,” Proceedings of the First IEEE Conference on Evolutionary Computation, Vol.1, pp. 345-349, 1994.
[14] E. P. Dadios, and D. J. Williams, “Non-conventional Control of the Flexible-Pole Cart Balancing Problem: Experimental Results,” IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol.28, pp. 895-901, 1998.
[15] E. P. Dadios, and N. Gunay, “Fuzzy Logic Based Neural Network Implemented Controller For Highly Nonlinear Systems,” International Journal of Knowledge-Based Intelligent Engineering Systems, Vol.4, No.4, pp. 254-262, 2000.

This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] F. O. Karray, and C. De Silva, “Soft Computing and Intelligent Systems Design,” PEARSON Addison Wesley, Edenburgh Gate, Harlow, Essex CM20 2JE, England, 2004.

[2] [2] D. Dasgupta, and Z. Michalewics, “Evolutionary Algorithms in Engineering Applications,” Springer-Verlag, Berlin Heidelberg, 1997.

[3] [3] J. Koza, “Genetic Programming: On the Programming of Computers by Means of Natural Selection,” MIT Press, 1992.

[4] [4] W. Langdon, “GP + Data Structure = Automatic Programming,” Kluwer Academic Publisher, New Your, 1994.

[5] [5] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Reading, MA: Addison-Wesley, 1989.

[6] [6] P. Ross, and D. Corne, “Applications of Genetic Algorithms,” Prentice Hall PTR, Englewood Cliffs, 1995.

[7] [7] J. H. Holland, “Adaptation in Natural and Artificial Systems,” Ann Arbor, MI: Univ. Michigan Press, 1975.

[8] [8] S. R. Ladd, “Genetic Algortihms in C++,” MIT Books, New York, 1997.

[9] [9] W. Wienholt, “A Refined Genetic Algorithm for Parameter Optimization Problems,” Proceedings of the Fifth International Conference on Genetic Algorithms, pp. 589-596, Morgan Kaufmann Publishers, 1993.

[10] [10] Z. Michalewicz, and M. Michalewicz, “Evolution Computation Techniques and their Application,” in IEEE International Conference on Intelligent Processing Systems, pp. 14-26, 1997.

[11] [11] D. Fogel, “An Introduction to Simulated Evolutionary Optimization,” IEEE Transactions on Neural Networks, Vol.5, No.1, pp. 3-14, 1994.

[12] [12] T. Hatanaka, K. Uosaki, H. Tanaka, and Y. Yamada, “Systems Parameter Estimation by Evolutionary Strategy,” Proceedings of the 35^th SICE Annual Conference, pp. 1045-1048, 1996.

[13] [13] G. W. Greenwood, A. Gufta, and K. Mcsweeney, “Scheduling Task in Multiprocessor Systems Using Evolutionary Strategies,” Proceedings of the First IEEE Conference on Evolutionary Computation, Vol.1, pp. 345-349, 1994.

[14] [14] E. P. Dadios, and D. J. Williams, “Non-conventional Control of the Flexible-Pole Cart Balancing Problem: Experimental Results,” IEEE Transactions on Systems, Man, and Cybernetics – Part B: Cybernetics, Vol.28, pp. 895-901, 1998.

[15] [15] E. P. Dadios, and N. Gunay, “Fuzzy Logic Based Neural Network Implemented Controller For Highly Nonlinear Systems,” International Journal of Knowledge-Based Intelligent Engineering Systems, Vol.4, No.4, pp. 254-262, 2000.

Genetic Algorithm On Line Controller for the Flexible Inverted Pendulum Problem

Elmer P. Dadios*, Patrick S. Fernandez**, and David J. Williams***

Elmer P. Dadios^, Patrick S. Fernandez^, and David J. Williams^