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Neuro-adaptive control of robotic manipulators

Published online by Cambridge University Press:  09 March 2009

S. Khemaissia  and
A.S. Morris
S. Khemaissia
Affiliation:
Department of Automatic Control and Systems EngineeringUniversity of SheffieldP.O. Box 600Mappin StreetSheffield SI 4DU (UK)
A.S. Morris
Affiliation:
Department of Automatic Control and Systems EngineeringUniversity of SheffieldP.O. Box 600Mappin StreetSheffield SI 4DU (UK)
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Summary

The need to meet demanding control requirements in increasingly complex dynamical control systems under significant uncertainties makes neural networks very attractive, because of their ability to learn, to approximate functions, to classify patterns and because of their potential for massively parallel hardware implementation. This paper proposes the use of artificial neural networks (ANN) as a novel approach to the control of robot manipulators. These are part of the general class of non-linear dynamic systems where non-linear compensators are required in the controller.

Information

Type
Research Article
Information
Robotica , Volume 11 , Issue 5 , September 1993 , pp. 465 - 473
Copyright
Copyright © Cambridge University Press 1993

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References

1.Ozaki, T., Suzuki, T., Furuhashi, T., Okuma, S. and Uchikawa, Y., “Trajectory control of Robotic Manipulators Using Neural Networks” IEEE Transactions on Industrial, Electronics 383, 195–202 (1991).CrossRefGoogle Scholar
2.Luh, J.Y.S., Walker, M.W. and Paul, R.P.C., “On-line computational scheme for mechanical manipulators” J. Dyn. Syst., Meas. Contr. 102, 69–76 (1980).CrossRefGoogle Scholar
3.Craig, J.J., Adaptive Control of Mechanical Manipulators (Addison-Wesley, Reading, Massachusetts, 1988).Google Scholar
4.Slotine, J.E. and Li, W., “Adaptive manipulator control: A case study” IEEE Transactions on Automatic Control 33(11), 995–1003 (1988).CrossRefGoogle Scholar
5.Katie, D. and Vukobratović, M., “Decomposed Connectionist Architecture for Fast and Robust Learning of Robot Dynamics”Int. Conf. on Robotics and Automation, Nice, France (1992) pp. 2064–2069. pp. 1902–1907.Google Scholar
6.Zomaya, A., Suddaby, M.E. and Morris, A.S., “Direct Neuro-Adaptive Control of Robot Manipulators”Int. Conf. on Robotics and Automation, Nice, France (1992) pp. 1902–1907.Google Scholar
6.Zalzala, A.M.S. and Morris, A.S. ”A neural network approach to adaptive robot control” Int. J. Neural Networks 2, 17–35 (1989).Google Scholar
8.Khemaissia, S. and Morris, A.S., “Fast-Robot System Identification Based on Neural Network Models”Proceedings of the 1992 IEEE International Symposium on Intelligent Control, Glasgow, UK (1992) pp. 13–15.Google Scholar
9.Sastary, S.S. and Isidori, A., “Adaptive control of linearizable systems” IEEE Transactions on Automatic Control 34, 1123–1131 (1989).CrossRefGoogle Scholar
10.Feng, G., Palaniswami, M. and Yao, Y., “Adaptive Tracking Control of Robot Manipulators in Cartesian Space Coordinates”Proc. of the American Control Conference 33 (1991) pp. 932–937.Google Scholar
11.An, C.H., Atkenson, C.G. and Hollerbach, J.M., Model-Based Control of Robot Manipulator (MIT Press, MA, Cambridge, 1989).Google Scholar
12.Khosla, P. and Kanade, T., “Parameter identification of robot dynamics”IEEE Conference on Decision and Control (1985) pp. 1754–1760.Google Scholar
13.Funahashi, K.C., On the approximate realization of continuous mapping by Neural Networks” Neural Networks 2, 183–192 (1989).CrossRefGoogle Scholar
14.Hornik, K., Stinchombe, M. and White, H., “Multi-layer feedforward networks are universal approximators” Neural Networks 2, 359–366 (1989).CrossRefGoogle Scholar
15.Cliz, M.K. and Isik, C., “Stability and Convergence of Neurologic Model Based Robotic Controllers”Int. Conf. on Robotics and Automation, Nice, France (1992) pp. 2051–2056.Google Scholar
16.Poo, A.N., JrAng, M.H., Teo, C.L. and Li, Qing, “Performance of a neuro-model-based robot controller: adaptability and noise rejection”, Intelligent Systems Engineering 1(1), 50–62 (1992).CrossRefGoogle Scholar