Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment

Ferguene, F. ; Toumi, R.

International Journal of Applied Mathematics and Computer Science, Tome 19 (2009) no. 1, pp. 113-126.

Résumé

Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

Détail

Keywords: force/position control, external structure, neural control, robot manipulator
Mots-clés : sterowanie położeniem, sterowanie siłowe, struktura zewnętrzna, sterowanie neuronowe, manipulator robota

Bibliographie

[1] Armstrong, B. and Khatib, O. (1986). Explicit dynamic model and inertial parameters of PUMA 560 arm, Proceedings of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, pp. 510-518.

[2] Boissonat, J.D. and Faverjon, B. (1988). Technique de la robotique, Hermès, Paris.

[3] Chiaverini, S. and Sciavesco, L. (1993). The parallel approach to force/position control of robotic manipulators, IEEE Transactions on Robotics and Automation 9(4): 361-373.

[4] Degoulange, E. (1993). Commande en effort d'un robot manipulateur à deux bras: Application au contrôle d'une chaîne cinématique fermée, Ph.D. thesis, Université Montpellier II.

[5] Chiu, C.S, Lian, K.Y. and Wu, T.C. (2004). Robust adaptive motion/ force tracking design for uncertain constrained robot manipulators, Automatica 40(12): 2111-2119.

[6] Dumas, R. and Samson, C. (1987). Robust nonlinear control of robotic manipulators: Implementation aspect and simulation, Internal report, INRIA, Rennes.

[7] Fraisse, P. (1994). Contribution à la commande robuste position/force des robots manipulateurs à architecture complexe: Application à un robot à deux bras, Ph.D. thesis, Université Montpellier II.

[8] Ferguene, F. and Toumi R. (2005). A neural approach to force/position parallel control of robotic manipulators application to the follow-up of trajectory in unknown stiffness environment, Proceedings of the International Conference on Computer Systems and Information Technology, Algiers, Algeria, No. 1, pp. 247-251.

[9] Haykin, S. (1999). Neural Networks: A Comprehensive Foundation, 2nd Edn., Prentice-Hall, Englewood Cliffs, NJ.

[10] Hogan, N.R. (1985). Impedance control: An approach to manipulator. Parts I, II and III, ASME Journal of Dynamics Systems, Measurement and Control 107(10): 1-24.

[11] Jung, S. and Hsia, T.C. (1995). On neural network application to robust impedance control of robot manipulators, Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan, Vol. 1, pp. 869-874.

[12] Jung, S. and Hsia, T.C. (1998). Neural network impedance force control of robot manipulator, IEEE Transactions on Industrial Electronics 45(3): 451-461.

[13] Jung, S. and Hsia, T.C. (2000). Robust neural force control design under uncertainties in robot dynamics and unknown environment, IEEE Transactions on Industrial Electronics, 47(2): 403-412.

[14] Jung, S. and Hsia, T.C. (2001). Experimental studies of neural network impedance forces control for robot manipulators, Proceedings of the IEEE International Conference on Robotics and Automation, Seoul, Korea, pp. 3453-3458.

[15] Khatib, O. and Burdick, J. (1986). Motion and force control of robot manipulators, Proceeding of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, pp. 1381-1386.

[16] Khatib, O. (1987). A unified approach for motion and force control of robot manipulators: The operational space formulation, ASME IEEE Journal of Robotics and Automation 3(1): 43-53.

[17] Kiguchi, K. and Fukuda, T. (1995). Robot manipulator contact force control application of fuzzy neural network, Proceedings of the IEEE International Conference on Robotics and Automation, Nagoya, Japan, Vol. 1, pp. 875-880.

[18] Kiguchi, K. and Fukuda, T. (1997). Intelligent position/force controller for industrial robot manipulators-application of fuzzy neural networks, IEEE Transactions on Industrial Electronics 44(6): 753-761.

[19] Nakawono, K. and Katagiri, M. (2003). Force and position control of robot manipulator using neurocontroller with GA based training, Proceedings of the IEEE International Symposium on Computational Intelligence in Robotics and Automation, Kobe, Japan, pp. 1354-1357.

[20] Mills, J. and Goldenberg, A. (1989). Forces and position control of manipulators during constrained motion task, IEEE Transactions on Robotics and Automation 5(1): 30-46.

[21] Perdereau, V. (1991). Contribution à la commande hybride force/position: Application à la coopération de deux robots, Ph.D. thesis, Université Pierre et Marie Curie.

[22] Raibert, M. and Craig, J. (1981). Hybrid position/force control of manipulators, Transactions of ASME Journal of Dynamics Systems Measurement and Control 103(2): 126-133.

[23] Saadia, N. (1997). Contribution à la commande hybride forceposition des robots compliants selon une approche neuronale, Ph.D. thesis, Université Paris XII.

[24] Saadia, N. Amirat, Y. and Djouani, J. (1997). Neural networks for force control of an assembly robot, Proceedings of the IFAC Conference on Control of Industrial Systems, Belfort, France, Vol. 2, pp. 551-557.

[25] Schuter, H. and Van Brussel, J. (1988). Compliant robot motion II with control approach based on external control loop, International Journal of Robotics Research 7(4): 18-33.

[26] Singh, S.K. and Popa, D.O. (1995). An analysis of some fundamental problems in adaptive control of force and impedance behavior: Theory and experiments, IEEE Transactions on Robotics and Automation 11(6): 912-921.

[27] Song, K.T and Li, H.P (1995). Design and experiment of fuzzy force controller for an industrial robot, Proceedings of the National Science Council, Republic of China 19(1): 26-36.

[28] Surdilovic, D. (1999). Robust robot compliant motion control using intelligent adaptive impedance approach, Proceedings of the IEEE International Conference on Robotics and Automation, Detroit, MI, USA, pp. 2128-2133.

[29] Yao, B. Chan, S.P. and Wang, D. (1992). Robust motion and force control of robot manipulators in the presence of environmental constraint uncertainties, Proceedings of the IEEE Conference on Decision and Control, Tucson, AZ, USA, pp. 1875-1880.

[30] Saadia, N. (1997). Robust motion and force control of robot manipulators in the presence of environmental constraint uncertainties, Proceedings of the IEEE Conference on Decision and Control, Tucson, AZ, USA, pp. 1875-1880.

[31] Yoshikawa, T. (2000). Force control of robot manipulators, Proceeding of the IEEE International Conference on Robotics and Automation, San Francisco, CA, USA, pp. 220-226.

[32] Whitcomb, L.L. (1997). Adaptive model-based hybrid control of geometrically constrained robot arms, IEEE Transactions on Robotics and Automation 13(1): 105-116.