Evolutionary Learning Acquisition of Optimal Joint Angle Trajectories of Flexible Robot Arm

Hiroyuki Kojima; Takahiro Hiruma

doi:10.20965/jrm.2006.p0103

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JRM Vol.18 No.1 pp. 103-110

doi: 10.20965/jrm.2006.p0103

(2006)

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Evolutionary Learning Acquisition of Optimal Joint Angle Trajectories of Flexible Robot Arm

Hiroyuki Kojima, and Takahiro Hiruma

Department of Mechanical System Engineering, Gunma University, 1-5-1 Tenjincho, Kiryu, Gunma 376-8515, Japan

Received:

April 28, 2005

Accepted:

September 12, 2005

Published:

February 20, 2006

Keywords:

evolutionary learning acquisition, flexible robot arm, optimal joint angle trajectory, residual vibration reduction, genetic algorithm

Abstract

This paper proposes the evolutionary learning acquisition method of the optimal joint angle trajectories of a flexible robot arm using the genetic algorithm is proposed, and the effects of the optimal joint angle trajectories obtained by the present evolutionary learning acquisition method on the residual vibration reduction are ascertained numerically and experimentally. In the construction of the evolutionary learning acquisition algorithm of the optimal joint angle trajectories, the joint angular velocity curves are depicted with fifth-order polynomials, and, by considering the boundary and constraint conditions, they are expressed by four parameters. Then, the residual vibrations of the flexible robot arm are expressed as a function of the chromosome consisting of four parameters, namely, four genes, and a fitness function of the genetic algorithm for the residual vibration reduction is defined. Furthermore, the numerical calculations have been carried out, and it is confirmed that the residual vibrations almost disappear. Moreover, the experimental results are demonstrated, and the usefulness of the present evolutionary learning acquisition method of the optimal joint angle trajectories of the flexible robot arm using the genetic algorithm is ascertained experimentally.

Cite this article as:

H. Kojima and T. Hiruma, “Evolutionary Learning Acquisition of Optimal Joint Angle Trajectories of Flexible Robot Arm,” J. Robot. Mechatron., Vol.18 No.1, pp. 103-110, 2006.

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References

[1] W. J. Book, O. Maizza Neto, and D. E. Whitney, “Feedback control of two beam, two joint systems with distributed flexibility,” Journal of Dynamic Systems, Measurement, and Control, Vol.97, No.4, pp. 424-431, 1975.
[2] Y. Sakawa, and Z. H. Luo, “Modeling and control of coupled bending and torsional vibrations of flexible beams,” IEEE Transactions on Automatic Control, Vol.34, No.9, pp. 970-977, 1989.
[3] Z. H. Luo, “Direct strain feedback control of flexible robot arms; new theoretical and experimental results,” IEEE Transactions on Automatic Control, Vol.38, No.11, pp. 1610-1622, 1993.
[4] R. L. Wells, J. K. Schueller, and J. Tlusty, “Feedforward and feedback control of a flexible robotic arm,” IEEE Control Systems Magazine, Vol.10, No.1, pp. 9-15, 1990.
[5] Y. Morita, H. Ukai, and H. Kando, “Robust trajectory tracking control of elastic robot manipulators,” Journal of Dynamic Systems, Measurement, and Control, Vol.119, No.4, pp.727-735, 1997.
[6] D. S. Kwon, and W. J. Book, “A time-domain inverse dynamic tracking control of a single-link flexible manipulator,” Transactions of the ASME, Journal of Dynamic Systems, Measurement, and Control, Vol.116, pp. 193-200, 1994.
[7] D. Knjazew, “OmeGA (A Component genetic algorithm for solving permutation and scheduling problems),” Kluwer Academic Publishers, 2002.

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

[1] [1] W. J. Book, O. Maizza Neto, and D. E. Whitney, “Feedback control of two beam, two joint systems with distributed flexibility,” Journal of Dynamic Systems, Measurement, and Control, Vol.97, No.4, pp. 424-431, 1975.

[2] [2] Y. Sakawa, and Z. H. Luo, “Modeling and control of coupled bending and torsional vibrations of flexible beams,” IEEE Transactions on Automatic Control, Vol.34, No.9, pp. 970-977, 1989.

[3] [3] Z. H. Luo, “Direct strain feedback control of flexible robot arms; new theoretical and experimental results,” IEEE Transactions on Automatic Control, Vol.38, No.11, pp. 1610-1622, 1993.

[4] [4] R. L. Wells, J. K. Schueller, and J. Tlusty, “Feedforward and feedback control of a flexible robotic arm,” IEEE Control Systems Magazine, Vol.10, No.1, pp. 9-15, 1990.

[5] [5] Y. Morita, H. Ukai, and H. Kando, “Robust trajectory tracking control of elastic robot manipulators,” Journal of Dynamic Systems, Measurement, and Control, Vol.119, No.4, pp.727-735, 1997.

[6] [6] D. S. Kwon, and W. J. Book, “A time-domain inverse dynamic tracking control of a single-link flexible manipulator,” Transactions of the ASME, Journal of Dynamic Systems, Measurement, and Control, Vol.116, pp. 193-200, 1994.

[7] [7] D. Knjazew, “OmeGA (A Component genetic algorithm for solving permutation and scheduling problems),” Kluwer Academic Publishers, 2002.