Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-28T09:45:19.318Z Has data issue: false hasContentIssue false
  • English
  • Français

Adaptive Learning Control for Cooperation of Two Robots Manipulating a Rigid Object with Model Uncertainties

Published online by Cambridge University Press:  09 March 2009

Dong Sun  and
F Xiaolun Shi Yunhui Liu
Dong Sun
Affiliation:
Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong, Shatin N.T. (Hong Kong).
F Xiaolun Shi Yunhui Liu
Affiliation:
Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong (Hong Kong).
Get access Rights & Permissions [Opens in a new window]

Summary

In this paper, an adaptive learning (A-L) control scheme is proposed for cooperation of two manipulators handling a rigid object with model uncertainties. For robots performing repetitive cooperating tasks, their operations are decomposed into two modes: the single operational mode and the repetitive operational mode on which the A-L controller is based. In the single operational mode, the controller is a learning based adaptive controller in which the robotic parameters are updated by using the information of the previous operation. In the repetitive operational mode, the controller is a model-based iterative learning controller. The advantages of the A-L controller lie in the fact that it can improve the transient performance as robots repeat operations at a high speed of the learning convergence. Simulation results ascertain that the A-L algorithm is effective in controlling two cooperated robots with model uncertainties.

Keywords

Two robotsCooperationAdaptive controlRigid objectLearning controlUncertainty.
Type
Article
Information
Robotica , Volume 14 , Issue 4 , July 1996 , pp. 365 - 373
Copyright
Copyright © Cambridge University Press 1996

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Zheng, Y.F. and Luh, J.Y.S.. “Control of two coordinated robots in motion” Proc. 25th IEEE Conf. Dec. and Control(1985) pp. 334–337.Google Scholar
Tarn., T.J., Bejcy, A.K. and Yun, X “Design of Dynamic Control of Two Cooperating Robot Arms: Closed Chanin Formulation” Proc. of IEEE Int. Conf. on Robotics and Automation (1987) pp. 7–13.Google Scholar
Hayati, S “Hybrid position/force control of multi-arm cooperating robots” Proc. of IEEE Int. Conf. on Robotics and Automation (1986) pp. 1375–1380.Google Scholar
Dauchez, P. and Fournier, A “Hybrid Control of a Two-Arm Robot for Complex Tasks” Robotics and Autonomous Systems 5, 323–332 (1989).CrossRefGoogle Scholar
Arimoto., S.Miyazaki, F. and Kawamura, S.. “Cooperative motion control of multiple robot arms or fingers” Proc. of IEEE Int. Conf. on Robotics and Automation (1987) pp. 1407–1412.Google Scholar
Koivo, A.J. and Unseren, M.J. “Reduced Order Model and Decoupled Control Architecture for Two ManipulatorsHolding a Rigid Object” Trans. ASME J. Dvn. Svs. Meas. and Contr. 113, 646–654 (1991).CrossRefGoogle Scholar
Tao, J.M. and Luh, J.Y.S.. “Robust Position and Force Control for A System of Multiple Redundant-Robots” Proc. of IEEE Int. Conf. on Robotics and Automation. (1992) pp. 2211–2216.Google Scholar
Okuma, S. and Ishiguro, A.. “A Neural Network Compensator for Uncertainties of Robotic Manipulators” Proc. of IEEE Int. Conf. on Robotics and Automation pp. 3303–3307.Google Scholar
Tao, J.M. and Luh, J.Y.S.. “Application of Neural Network with Real-Time Training to Robust Position/Force Control of Multiple Robots” Proc. of IEEE Int. Conf. on Robotics and Automation (1993) pp. 142–148.Google Scholar
Dubowski, S. and DesForges, D. T.. “The application of model referenced adaptive control to robotic manipulators” ASME J. Dvnamic Svst., Meas., Contr. 193 (09., 1979).Google Scholar
Horowitz, R. and Tomizuka, M.. “An adaptive control scheme for mechanical manipulators- compensation of nonlinearity and decoupling control”. ASME Paper, 8Q-WA/DSC-6 (1980).Google Scholar
Koivo, A.J.. “Adaptive Poisition-Velocity-Force Control of Two Manipulators” Proceedings of 24th Conferenceon Decision and Control(1984) pp. 1529–1532.Google Scholar
Slotine, J.J.E. and Li, W.. “On the Adaptive Control of Robot Manipulators” Int. J. Robotics Research. 6(3) 49–59 (1987).CrossRefGoogle Scholar
Arimoto., S.Kawamura, S. and Miyazaki, F.. “Bettering Operation of Robots by Learning” J. of Robotic Systems 1, 123–140 (1984).CrossRefGoogle Scholar
Arimoto, S.. “Mathematical Theory of Learning with Applications to Robot Control” Proc. of 4th Yale Workshop on Applications of Adaptive Svstems Theorv, Yale Univ., New Haven. CT (1985) pp. 379–388.Google Scholar
Kawamura., S.Miyazaki, F. and Arimoto, S.. “Realization of Robot Motion Based on a Learning Method” IEEE Transaction on Systems. Man and Cybernetics. 18, 126–133 (1988).CrossRefGoogle Scholar
Bondi., P.Casalino, G. and Gambardella, L.. “On the Iterative Learning Control Theory for Robotic Manipulators” IEEE Journal of Robotics and Automation 4(1) 14–22(1988).CrossRefGoogle Scholar
Arimoto, S.and Miyazaki, F.. “Stability and Robustness of PID Feedback Control for Robot Manipulators of Sensory Capability”. Int. Svm. on Rob. Res. (1984) pp. 783–799.Google Scholar
Liu, Y.H. and Arimoto, S.. “Distributively Controlling Two Robots Handling An Object In The Task Space Without any Communication” (to be published in IEEE Trans, on Automatic Control. 1996).Google Scholar
Mayeda, H.Osuka, K. and Kangawa, A. “A New Identification Method for Serial Manipulator Arm” Proc. of IF AC World Congress. Budapest. Hundary, pages 2429–2434 (1984) pp. 2429–2434.Google Scholar
Atkeson., C.G.An, C.G. and Hollerbach, J.M.. “Estimation of inertial parameters of manipulator loads and links” Int. J. Robotics Research 5, No. 3. 101–119 (1996).CrossRefGoogle Scholar
Ince, E.L.. Ordinary Defferential Equations (Dover Publications. 1926).Google Scholar