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標題: 類神經網路控制平面雙機械臂挾持彈性體之應用
Neural Network Control of Planar Dual-Arm Robot Systems with Flexible Object
作者: 陳奕亨
Chen, Yi-Heng
關鍵字: neural network;類神經網路;dual-arm;flexible link;雙機械臂;彈性體
出版社: 機械工程學系所
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[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. 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[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. 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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.
其他識別: U0005-1808200814210600
Appears in Collections:機械工程學系所

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