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Iterative Generation of Virtual Reference for a Manipulator

Published online by Cambridge University Press:  09 March 2009

M. Yamakita  and
K. Furuta
M. Yamakita
Affiliation:
Department of Control Engineering, Tokyo Institute of Technology, Oh-Okayama, Meguro-ku, Tokyo (Japan)
K. Furuta
Affiliation:
Department of Control Engineering, Tokyo Institute of Technology, Oh-Okayama, Meguro-ku, Tokyo (Japan)
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Summary

Virtual reference is defined as the signal for a closed-loop System so that the response tracks the desired path perfectly. It is proposed to obtain a virtual reference signal suitable for a manipulator to track a predetermined trajectory using repetitive operations. Thus the algorithm ensures that the difference between the desired and the manipulator trajectories will become negligible as the number of operations approaches infinity if the manipulator is compensated for properly by local feedback. This algorithm uses a dual System to recursively improve the reference signal. Such a System has the advantage of being simple to design. The derivation of this algorithm is based on functional analysis. The effectiveness is confirmed by two experimental results.

Keywords

Virtual referenceTrajectoryClosedloop SystemFunctional analysis
Type
Article
Information
Robotica , Volume 9 , Issue 1 , January 1991 , pp. 71 - 80
Copyright
Copyright © Cambridge University Press 1991

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References

1.Arimoto, S., Kawamura, S. and Miyazaki, F., “Bettering operation of robots by learning”, J. Robotic Systems 1, No. 2, 123–140 (1984).CrossRefGoogle Scholar
2.Kawamura, S., Miyazaki, F. and Arimoto, S., “Iterative Learning Control For Robotic Systems” Proc. of IECON '84, 393/ 398 (10, 1984).Google Scholar
3.Arimoto, S., Kawamura, S. and Miyazaki, F., “Can Mechanical Robots Learn by Themselves?” Proc. Robotics Res. 2nd Int. Symp. (MIT Press, Cambridge, Mass., 1985) pp. 127–134.Google Scholar
4.Kawamura, S., Miyazaki, F. and Arimoto, S., “Hybrid position/force control of manipulators based on learning method” Proc. '85 Int. Conf. on Advanced Robotics,Tokyo, 235–242 (1985).Google Scholar
5.Mita, T. and Kato, E., “Iterative Control of Robot Manipulator” Proc. 15th ISIR 665/ 672 (1985).Google Scholar
6.Furuta, K. and Yamakita, M., “A Design of Learning Control System for Linear Multivariable Systems”, Trans. of Society of Instrument and Control Engineers (in Japanese) 22, No. 12, 6–13 (1986).Google Scholar
7.Furuta, K. and Yamakita, M., “Iterative genration of optimal input of a manipulator” Proc. of IEEE Robotics and Automation 579–583, (1986).Google Scholar
8.Yamamoto, Y., “On the Convergence Condition of the Learning Control Scheme for Linear Systems” Trans. of Society of Instrument and Control Engineers (in Japanese) 24, No. 12, 1331–1333 (1988).CrossRefGoogle Scholar
9.Uchiyama, M., “Formation of High-Speed Motion Pattern of a Mechanical Arm by Trial” Trans. of Society of Instrument and Control Engineers (in Japanese) 14, No. 6, 706–712 (1978).Google Scholar
10.Togai, M. and Yamano, O., “Analysis and Its Application to Controlling Industrial Robots” Proc. of IEEE Robotics and Automation 248–253 (1986).Google Scholar
11.Omata, T.et al., “Nonlinear Repetitive Control with Application to Trajectory Control of Manipulators” J. Robotic Systems 4, No. 5, 631–652 (1987).CrossRefGoogle Scholar
12.Aticeson, C.G.et al., “Model-Based Robot Learning” Proc. of The 4th Int. Sym. Robotics Res. 103–110 (1987).Google Scholar
13.Hauser, J.E., “Learning Control for a Class of Nonlinear Systems” Proc. of IEEE CDC 859–860 (1987).Google Scholar
14.Leondes, J., Modern Control Systems Theory, McGraw-Hill, New York, 1965) pp. 97–120.Google Scholar
15.Luenberger, D., Optimization by Vector Space Methods (Wiley, New York, 1969).Google Scholar
16.Yoshizawa, T., “Stability theory and the existence of periodic solutions and almost periodic solutions” Applied Mathematical Sciences 14, (Springer-Verlag, Berlin, 1975).Google Scholar
17.Morisada, M., “A Study on Trajectory Planning System for a Robot Arm” Bachelor Thesis at Control Eng. in Tokyo Instituts of Tech, (in Japanese 1986).Google Scholar