Please use this identifier to cite or link to this item:
標題: 類神經式阻抗控制法於撓性關節機械臂力量控制之應用
Neural-Impedance Control for flexible joint robots contact tasks
作者: 劉醇達
Liou, Chun-Da
關鍵字: Flexible joint;撓性關節;Impedance Control;Neural Network;阻抗控制;類神經網路
出版社: 機械工程學系所
引用: [1]Hogan, N., 1985, “Impedance Control: an Approach to Manipulation. Part I - Theory,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol.107, pp. 1-7. [2]Hogan, N., 1985, “Impedance Control: an Approach to Manipulation. Part II - Implementation,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol.107, pp. 8-16. [3]Hogan, N., 1985, “Impedance Control: an Approach to Manipulation. Part III - Application,” ASME Journal of Dynamic Systems, Measurement, and Control, Vol.107, pp. 17-24. [4]Hogan, N., 1987, “Stable Execution of Contact Tasks Using Impedance Control,” IEEE International Conference on Robotics & Automation, Vol. 2, pp. 1047-1054. [5]Raibert, M.H. and Carig, J.J., 1981, “Hybrid Position/Force Control of Manipulators,” ASME Journal of Dynamic Systems, Measurement, and Control, pp.126-133. [6]Mason, M.T., 1981, “Compliance and Force Control for Computer Controlled Manipulators,” IEEE Transaction on Systems, Man and Cybernetics , Vol. SMC-11, pp.418-432. [7]Lin, S.T. and Huang, A.K., 1998, “Hierarchical Fuzzy Force Control for Industrial Robots,” IEEE Transactions on Industrial Electronics. [8]Lin, S.T. and Huang, A.K., 1997, “Position-Based Fuzzy Force Control for Dual Industrial Robots,” Journal of Intelligent & Robotic System, Vol. 19, No. 4, pp. 393-409. [9]Tarokh, M. and Bailey, S., 1996, “Force Tracking with Unknown Environment Parameters using Adaptive Fuzzy Controllers,” Proc. of the 1996 IEEE Int. Conference on Robotics and Automation, pp. 270-275 [10]Seraji, H. and Colbaugh, R., 1997, “Force Tracking in Impedance Control,” The International Journal of Robotics Research, Vol. 16, No. 1, pp. 97-117. [11]Lin, S.T. and Lee, J.S., 1996, “Adaptive Impedance Control For Robot Contact Tasks,” Journal of Engineering, National Chung-Hsing University, Vol.7, No. 1, pp.55-68. [12]Kuschewski, J. G., Hui, S. and Zak. S.H., 1993“Application of Feedforward Neural Network to Dynamical System Identification and Control,” IEEE Transaction on Control Systems Technology, Vol. 1, No. 1, pp.37-49. [13]Yamada, T., and Yabuta, T., 1993, “Dynamic System Identification Using Neural Networks,” IEEE Transactions on Systems, Man and Cybernetics, Vol. 23, No. 1. [14]Meng, Q.H.M. and Yao, Y.Y., 1994, “Design of Neural Network Controller for Robots Using Regressor Dynamics,” Proc. of IEEE International Conference on Neural Networks, Vol 5, pp. 2743-2748. [15]Fukuda, T., Shibata, T., Tokita, M. and Mitsuoka, T., 1992, “Neuromophic Control: Adaptation and Learning,” IEEE Transactions on Industrial Electronics, Vol. 39, No. 6, pp.21-27. [16]Okuma, S., Ishiguro, A., Furuhashi, T., Uchikawa, Y., 1990, “A Neural Network Compensator for Uncertainties of Robots Manipulators,” Proc. of IEEE Conference on Decision and Control, pp. 3303-3308. [17]Yegerlehner, J.D. and Meckl, P.H., 1992, “Neural Network Control for a Two-Link Manipulator Undergoing Large Payload Changes,” ASME Neural Networks in Manufacturing and Robotics, PED-Vol. 57, pp. 105-116. [18]Lin, S.T. and Tsai, H.C., 1997, “Impedance Control with On-line Neural Network Compensator for Dual-Arm Robots,” Journal of Intelligent & Robotic Systems, Vol. 18, No. 1, pp.87-104. [19]Tsuji, T., Ito, K. and Morasso, P.G., 1996, “Neural Network Learning of Robot Arm Impedance in Operational Space,” IEEE Transactions on System, Man, and Cybernetics-Part B: Cybernetics, Vol. 26, No. 2, pp. 290-298. [20]P. Tomei, 1991, “A simple PD control for robots with elastic joints,” on Autom. Contr., Vol. 36, no. 10, pp. 1208-1213, October 1991. [21]M. G. Forrest-Barlach and S. M. Babcock, “Inverse dynamics position control of a compliant manipulator,” Proc. Of IEEE Int. Conf. on Robotics and Automation, San Francisco, pp. 196-205,1986; IEEE J. of Robotics and Automation, RA-3, pp. 75-83,1987. [22]M. W. Spong and H. Sira-Ramirze, “Robust control of nonlinear sysems,” Proc. of the Ameri. Contr. Conf., Seattle, 1986. [23]M. W. Spong, “Adaptive control of flexible joint manipulators,” Syst. Contr. Lett., Vol. 13, pp. 15-21,1989. [24]F. T. Mrad and S. Ahmad, “Adaptive control of flexible joint robots using position and velocity feedback,” Int. J. Control, Vol. 55, no. 5, pp. 1255-1277, 1992 [25]S. Nicosia and P. Tomei, “A method to design adaptive controllers of flexible joint robots,” Proc. of the 1992 IEEE Int. Conf. on Rob. And Auto., Nice, France, May 1992. [26]G. Ferretti、G. A. Magnani and P. Rocco, “ Impedance control for elastic joints industrial manipulators,” IEEE Transactions on robotics and automation, Vol. 20, No. 3, June 2004 [27]A. C. Huang and M. C. Chien, “Design of a Regressor-free Adaptive Impedance Controller for Flexible-joint Electrically-driven Robots,” IEEE ICIEA, 2009 [28]P. Rocco, On “Stability and control of elastic joint robotic manipulators during constrained-motion tasks ,” IEEE Transactions on robotics and automation, Vol. 13, No. 3, June 1997 [29]R. Ozawa and H. Kobayashi, “A new impedance control concept for elastic joint robots ,” Proc. of the 2003 IEEE Int. Conf. on Rob. And Auto., Taipei, Taiwan, September 14-19. [30]王進德、蕭大全, “類神經網路與模糊控制理論入門,”全華科技圖書股份有限公司, 1994. [31] Kawato, M., Uno, Y., Isobe, M. and Suzuki, R., 1988, “Hierarchical Network Model for Voluntary Movement with Application to Robotics,” IEEE Control Systems Magazine, ”pp. 8-16. [32] Psaltis, D., Sideris, A. and Yamamura, A., 1988, “A Multilayered Neural Network Controller,” IEEE Control Systems Magazine, ” pp. 17-21.

A neural-impedance controller for flexible joint robots contact motion is proposed in this thesis. The objective of this controller is to achieve desired contact force in face of unknown environment . This controller consists of an impedance controller and an online-training neural network. Neural network is used to learn the relation between the contact force and the reference position input in the impedance system with unknown environment. If it consists errors in contact force signal, the online-training mechanism can regulate the weight in the neural network to make the signal fits the force command. Simulation results show that the proposed neural-impedance controller has satisfactory performance.
其他識別: U0005-0908201018261800
Appears in Collections:機械工程學系所

Show full item record
TAIR Related Article

Google ScholarTM


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.