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研究生:陳奕亨
研究生(外文):Yi-Heng Chen
論文名稱:類神經網路控制平面雙機械臂挾持彈性體之應用
論文名稱(外文):Neural Network Control of Planar Dual-Arm Robot Systems with Flexible Object
指導教授:林仕亭
指導教授(外文):Shi-Ting Lin
學位類別:碩士
校院名稱:國立中興大學
系所名稱:機械工程學系所
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2008
畢業學年度:96
語文別:中文
論文頁數:109
中文關鍵詞:類神經網路雙機械臂彈性體
外文關鍵詞:neural networkdual-armflexible link
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在雙機械臂挾持物件的系統,可以視為閉鍊(closed chain)的多體機械系統(multibody mechanical system);欲建立此閉鍊系統的動態方程式,可應用Lagrange Multiplier定理將系統的拘束方程式代入動態方程式中,就可以得到拘束動態方程式。透過求解系統的拘束動態方程式,可求得Lagrange multipliers;經由轉換計算可以得到物件的受力,進而進行力量控制。
本文將類神經網路控制法導入雙機械臂Lagrange multiplier控制法中,藉由類神經網路反向動力的學習方式來克服原本雙機械臂Lagrange multiplier控制法中起始系統參數的不確定性的問題,也因為如此就不會有參數估測誤差導致系統控制效果不佳。由電腦控制模擬的結果可以知道,可以利用此理論架構進行雙機械臂進行多種工作型態下的位置及力量控制。本文在最後也探討雙機械臂挾持彈性體的系統,使用修正過後的雙機械臂動態方程式,對雙機械臂挾持彈性體進行壓縮彈性體的控制,最後透過電腦的模擬結果可以知道,可以利用此種控制方法來對雙機械臂壓縮彈性體進行控制。
Dual-arm robots holding the object can be seen as a closed chain multibody mechanical system. To formulate the equation of motion of the closed chain multibody mechanical system, one can introduce the constrained equations into equations of motion by applying Lagrange Multiplier theorem, and then obtain the constrained equations of motion. Solving the constrained equations of motion, one can get the Lagrange multipliers, which can be used to calculate the force acting on the object held by dual-arm robots, and then make force control.
In this thesis, we take the concept of neural network into Dual-arm robots control. We overcome the problem of initial parameter uncertainty in Dual-arm robots control by using Neural Network Inverse Dynamics, to avoid error due to parameter uncertainty. From the results of simulations, we can use this theory for simultaneous position/force control of dual-arm robot in many cases. In the end of this thesis, we also treat about dual-arm robot system with flexible object. We use the modified dynamic equation of dual-arm robots to develop the control system. From the results of simulations, we can use this system to control the dual-arm robot with flexible object.
中文摘要 I
ABSTRACT II
致謝 III
目錄 IV
圖目錄 VII
表目錄 XI
第一章 緒論 1
1.1研究動機 1
1.2文獻回顧 4
1.3論文大綱 10
第二章 類神經網路理論 11
2.1類神經網路簡介 11
2.2類神經元 12
2.3類神經網路架構 13
2.4類神經網路訓練法介紹 14
第三章 動態方程式及機械臂控制法 19
3.1動態方程式之推導 20
3.1.1 Lagrange方程式 20
3.1.2平面機械臂動態方程式的推導 21
3.1.3 Lagrange Multiplier定理 28
3.1.4平面雙機械臂拘束方程式 29
3.2機械臂控制法介紹 33
3.2.1計算力矩法 33
3.2.2類神經網路反向動力控制法 34
第四章 Lagrange Multiplier控制法 37
4.1 Lagrange Multiplier控制法之理論架構 37
4.1.1雙機械臂基本架構及系統參數 37
4.1.2切斷點之拘束方程式 39
4.1.3 Lagrange Multiplier控制法 42
4.2卡式座標之Lagrange Multiplier控制法 45
4.2.1控制策略設計 45
4.2.2穩定性分析 47
4.3 Lagrange Multiplier類神經網路控制法 51
4.3.1類神經網路反向動力控制系統設計 51
4.3.2類神經網路系統的訓練與學習 54
4.4模擬之結果與討論 57
4.4.1雙機械臂物件搬運控制模擬 58
4.4.2雙機械臂鉗子挾持控制模擬 65
4.4.3雙機械臂零件組裝控制模擬 72
4.4.4結果與討論 80
第五章 雙機械臂挾持彈性體 81
5.1挾持彈性體的拘束動態方程式推導 81
5.1.1動態方程式推導 81
5.1.2挾持彈性體切斷點之束方程式 91
5.1.3挾持彈性體拘束動態方程式 92
5.2 Lagrange Multiplier控制法運用在挾持彈性體的系統 94
5.2.1運用在挾持彈性體的Lagrange Multiplier控制法 94
5.2.2討論Lagrange Multiplier控制法控制策略 98
5.3模擬結果與討論 99
第六章 結論及未來展望 103
參考文獻 105
[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] 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.
[6] 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.
[7] Mills, J. K., Goldenberg, A. A., ” Force and Position Control of Manipulators During Constrained Motion Tasks,” IEEE Trans. in Robotics and Automation, Vol. 5, No. 4, Feb. 1989, pp. 30-46.
[8] Peng, Z. X., Aadchi, N.,“ Position and Force Control of Manipulators without Using Force Sensors,” JSME International Journal, Vol. 35, No. 2, pp. 252-258.
[9] Wang, D., McClamroch, N. H.,” Position/Force Control Design for Constrained Mechanical System: Lyapunov’s Direct Method,”.
[10] Hu, Y. R., Goldengerg, A. A., Zhou, C.,” Motion and Force Control of Coordinated Robots During Constrained Motion Tasks,” International Journal of Robotics Research, Vol. 14, No. 4, 1995 Aug., pp. 351-365.
[11] Wittenburg, J.,” Nonlinear Equations of Motion for Arbitary System of Interconnected Rigid Bodies,” Symposium on the Dynamics of Multibody System, Munich, Germany, Pro. Published by Spring-Verlag, 1987, K. Magnus, editor.
[12] Wittenburg, J., Wolz, U.,” MESA VERGE: A Symbolic Program for Nonlinear Articulater-Rigid-Body Dynamics,” ASME, 85-DET-151.
[13] 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.
[14] 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.
[15]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.
[16]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.
[17] Wang, D., McClamroch, N. H.,” Feedback Stabilization and Tracking of Constrained Robots,” IEEE Transaction on Automatic Control, Vol. 33, No.5, May, 1998, pp 419-426.
[18] Yabuta, T., Chona, A. J., Beni, G.,” On the Asymptotic Stability of the Hybrid Position/Force Control Scheme for Robot Manipulators,” Proc. IEEE Conf. Robotics Automat., 1988, p.338
[19] Wen, J.T., Kenneth, K.,“Motion andforce control of multiple robotic manipulators,” Automatica, vol. 28, no. 4, pp. 729–743, 1992.
[20] Yao, B. et al., “VSC coordinated control of two manipulator arms in the presence of environmental constraints,” IEEE Trans. Autom. Control,vol. 37, no. 11, pp. 1806–1812, Nov. 1992.
[21] Hsu, P., “Coordinated control of multiple manipulator systems,” IEEE Trans. Robot. Autom., vol. 9, no. 4, pp. 400–410, Aug. 1993.
[22] Zhu, W.H.and Schutter, J.D., “Experimental verifications of virtual-decomposition-based motion/force control” IEEE Transactions on Robotics and Automation, v 18, n 3, June, 2002, p 379-386.
[23] Sun, D.and Mills, J.K., “Adaptive synchronized control for coordination of multirobot assembly tasks,” IEEE Trans. Robot. Autom., vol. 18, no.4, pp. 498–510, Aug. 2002.
[24] Zhu, W.H. “On adaptive synchronization control of coordinated multirobots with flexible_rigid constraints” IEEE Transactions on Robotics, v 21, n 3, June, 2005, p 520-525.
[25] Dauchez, P., “A vision_position_force control approach for performing assembly tasks with a humanoid robot” Proceedings of 2005 5th IEEE-RAS International Conference on Humanoid Robots, v 2005, Proceedings of 2005 5th IEEE-RAS International Conference on Humanoid Robots, 2005, p 277-282.
[26] Li, Z.J., Ming, A., Xi, N., Xie, Z.X., Gu, J.G., Shimojo, M., “Collision-tolerant control for hybrid joint based arm of nonholonomic mobile manipulator in human-robot symbiotic environments” Proceedings - IEEE International Conference on Robotics and Automation, v 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation, 2005, p 4037-4043.
[27] Wu, H., Sun, F.C., Sun, Z.Q. and Wu, L.C., “Optimal trajectory planning of a flexible dual-arm space robot with vibration reduction” Journal of Intelligent and Robotic Systems: Theory and Applications, v 40, n 2, June, 2004, p 147-163
[28]Zurada, J.M., 1992, Introduction to Artificial Neural Systems. West Publishing Company.
[29]Psaltis, D., Sideris, A. and Yamamura, A., 1988, “A Multilayered Neural Network Controller,” IEEE Control Systems Magazine, pp. 17-21.
[30]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.
[31] Paul, R. P. “ Modeling, Trajectory Calculation, and Servoing of a Computer Controlled Arm,” Technical Report AIM-177, Stanford University Artificial Intelligence Laboratory, 1972.
[32] Markiewicz, B.,“ Analysis of the Computed Torque Drive Method and Comparison with Conventional Position Servo for a Computed-Controlled Manipulator,” Jet Propulsion Laboratory Technical Memo 33-601, March 1973.
[33] Bejczy, A.,“ Robot Arm Dynamics and Control,” Jet Propulsion Laboratory Technical Memo 33-669, February 1974.
[34] Lightbody, G., Wu, W.H. and Irwin, G.W. et al., “ Control application for feedforward networks,” in Neural Networks for Control, T. W. Miller et al., Eds. Cambridge, MA: MIT Press, 1990, pp. 51–71.
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