隨著智慧自動化的發展，機械手臂於各領域的應用愈來愈廣泛。當機械手臂與外界接觸時，機械手臂與環境動態耦合可能導致機械手臂不穩定，機械手臂與環境之間的反應力須被適當處理，故機械手臂與環境接觸之課題也是熱門的研究重點領域，應用層面包含：零件組裝、插件、拋光磨光、醫療用機器人和人機互動協作。本論文研究機械手臂末端點與外界環境接觸時之響應，於順應控制直覺教導架構上實作外力估測方法，並使用混合順應控制架構，將工作空間分成位置控制子空間與力量控制子空間，可於循跡過程中同時進行力量追蹤。本論文實現混合阻抗控制、適應性混合阻抗控制等架構，加入適應性方法可以補償環境位置與環境剛性的不確定因素，並探討調整力量控制子空間中阻抗模型係數對機械手臂系統力量暫態響應之影響，上述控制架構不須機械手臂模型，且可於未知環境資訊下進行力量追蹤。為了提升力量追蹤效果，避免當位置子空間機械手臂移動過快或環境高低位置變化過大時易造成較大的力量誤差，故本論文提出適應性混合變阻抗控制架構，利用誤差比例來調整阻抗模型之係數，改善機械手臂末端點之力量暫態響應，以降低力量響應追蹤誤差。最後，本論文使用上述控制架構，針對平面、斜坡和突起三個不同的環境進行特定軸向力量控制。實驗結果顯示，本論文所提出的方法相較於前兩者確實有較佳的追蹤效果。
關鍵字：機械手臂、順應控制、混合阻抗控制、力量控制

With the development of automation and artificial intelligence, robot manipulators have been widely used in diverse fields. When a robot arm interacts with the environment, the coupling of the robot and the environment may cause instability, and the contact force should be appropriately handled. Contact operation between the robot and the environment is a popular research topic, including assembly, peg-in-hole, deburring, grinding, milling, cutting, polishing, medical robots and human-robot interaction. This thesis studies the dynamic response between the robot end-effector and the environment, and external force observers are implemented in the compliance control-based intuitive teaching structure. Moreover, the task space is divided into two subspaces, position-controlled and force-controlled, to develop a hybrid compliance control architecture which can track the recorded trajectory and desired force simultaneously. This thesis implements hybrid impedance control and adaptive hybrid impedance control structures, and force tracking transient response due to coefficients of the impedance model will be discussed. When performing the above two structures on force tracking, the robot model and environment information are not required. To avoid larger force tracking error due to fast-moving tasks in the position-controlled subspace or drastic environment position variation, an adaptive hybrid variable impedance control structure is proposed. One can use the force tracking error proportion on adjustment of the impedance coefficients to improve the transient force response of the end-effector and reduce force tracking error. Finally, the hybrid compliance control schemes are applied to execute force tracking on the z-axis in three different environments: plane, slope, and protrusion. As indicated in the experimental results, the proposed scheme has better tracking performance than previous methods.

[1] 華碩電腦股份有限公司，
https://zenbo.asus.com/tw/product/zenbo/gallery/ [Jun 9, 2020]
[2] KUKA Robotics，
https://www.kuka.com/zh-tw/%E7%94%A2%E5%93%81/%E6%A9%9F%E5%99%A8%E4%BA%BA%E7%B3%BB%E7%B5%B1/industrial-robots/lbr-iiwa [Jun 9, 2020]
[3] Techman Robot，https://www.tm-robot.com/en/regular-payload/ [Jun 9, 2020]
[4] A. Calanca, R. Muradore, and P. Fiorini, “A Review of Algorithms for Compliant Control of Stiff and Fixed-Compliance Robots,” IEEE/ASME Transactions on Mechatronics, vol. 21, no. 2, pp. 613-624, 2016.
[5] C. Ott, R. Mukherjee, and Y. Nakamura, “A Hybrid System Framework for Unified Impedance and Admittance Control,” Journal of Intelligent & Robotic Systems, vol. 78, no. 3-4, pp. 359-375, 2014.
[6] B. Yang, D. Zhai, W. Lyu, and Y. Xia, “Event-Based Hybrid Impedance and Admittance Control,” in Proceedings of Chinese Control Conference (CCC), 27-30 Jul. 2019, pp. 4596-4601
[7] C. Mei, J. Yuan, and R. Guan, “Adaptive Unified Impedance and Admittance Control Using Online Environment Estimation,” in Proceedings of IEEE International Conference on Robotics and Biomimetics (ROBIO), 12-15 Dec. 2018, pp. 1864-1869
[8] F. Ferraguti, C. Talignani Landi, L. Sabattini, M. Bonfè, C. Fantuzzi, and C. Secchi, “A Variable Admittance Control Strategy for Stable Physical Human–Robot Interaction,” The International Journal of Robotics Research, vol. 38, no. 6, pp. 747-765, 2019.
[9] F. Ficuciello, L. Villani, and B. Siciliano, “Variable Impedance Control of Redundant Manipulators for Intuitive Human–Robot Physical Interaction,” IEEE Transactions on Robotics, vol. 31, no. 4, pp. 850-863, 2015.
[10] A. Lecours, B. Mayer-St-Onge, and C. Gosselin, “Variable Admittance Control of a Four-Degree-of-Freedom Intelligent Assist Device,” in Proceedings of IEEE International Conference on Robotics and Automation, 14-18 May 2012, pp. 3903-3908
[11] G. Kang, H. S. Oh, J. K. Seo, U. Kim, and H. R. Choi, “Variable Admittance Control of Robot Manipulators Based on Human Intention,” IEEE/ASME Transactions on Mechatronics, vol. 24, no. 3, pp. 1023-1032, 2019.
[12] D. Ryu, J. Song, S. Kang, and M. Kim, “Frequency Domain Stability Observer and Active Damping Control for Stable Haptic Interaction,” IET Control Theory & Applications, vol. 2, no. 4, pp. 261-268, 2008.
[13] F. Dimeas, and N. Aspragathos, “Online Stability in Human-Robot Cooperation with Admittance Control,” IEEE Transactions on Haptics, vol. 9, no. 2, pp. 267-278, 2016.
[14] A. Campeau-Lecours, M. Otis, P.-L. Belzile, and C. Gosselin, “A Time-Domain Vibration Observer and Controller for Physical Human-Robot Interaction,” Mechatronics, vol. 36, pp. 45-53, 2016.
[15] H. Li, I. Paranawithana, L. Yang, T. S. K. Lim, S. Foong, F. C. Ng, and U. Tan, “Stable and Compliant Motion of Physical Human–Robot Interaction Coupled With a Moving Environment Using Variable Admittance and Adaptive Control,” IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 2493-2500, 2018.
[16] A. D. Luca, A. Albu-Schaffer, S. Haddadin, and G. Hirzinger, “Collision Detection and Safe Reaction with the DLR-III Lightweight Manipulator Arm,” in Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 9-15 Oct. 2006, pp. 1623-1630
[17] R. Cui, L. Chen, C. Yang, and M. Chen, “Extended State Observer-Based Integral Sliding Mode Control for an Underwater Robot with Unknown Disturbances and Uncertain Nonlinearities,” IEEE Transactions on Industrial Electronics, vol. 64, no. 8, pp. 6785-6795, 2017.
[18] G. Sebastian, Z. Li, V. Crocher, D. Kremers, Y. Tan, and D. Oetomo, “Interaction Force Estimation Using Extended State Observers: An Application to Impedance-Based Assistive and Rehabilitation Robotics,” IEEE Robotics and Automation Letters, vol. 4, no. 2, pp. 1156-1161, 2019.
[19] T. Ren, Y. Dong, D. Wu, and K. Chen, “Collision Detection and Identification for Robot Manipulators based on Extended State Observer,” Control Engineering Practice, vol. 79, pp. 144-153, 2018.
[20] S. H. Chien, J. H. Wang and M.-Y. Cheng, “Performance Comparisons of Different Observer- Based Force-Sensorless Approaches for Impedance Control of Collaborative Robot Manipulators,” in Proceedings of 3rd IEEE International Conference on Industrial Cyber Physical Systems (ICPS), Tampere, Finland, 2020.
[21] H. Seraji, “Adaptive Admittance Control: An Approach to Explicit Force Control in Compliant Motion,” in Proceedings of the 1994 IEEE International Conference on Robotics and Automation, 8-13 May 1994, pp. 2705-2712
[22] G. Zeng, and A. Hemami, “An overview of robot force control,” Robotica, vol. 15, no. 5, pp. 473-482, 1997.
[23] L. Villani, and J. De Schutter, Force Control, Berlin, Springer, 2008.
[24] D. Whitney, “Historical Perspective and State of the Art in Robot Force Control,”in Proceedings of 1985 IEEE International Conference on Robotics and Automation, 25-28 March 1985, vol. 2, pp. 262-268
[25] M. H. Raibert, and J. J. Craig, “Hybrid Position/Force Control of Manipulators,” ASME Journal of Dynamic Systems, Measurement, and Control, vol. 103, no. 2, pp. 126-133, 1981.
[26] M. T. Mason, “Compliance and Force Control for Computer Controlled Manipulators,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 11, no. 6, pp. 418-432, 1981.
[27] N. Hogan, “Impedance Control: An Approach to Manipulation: Part I—Theory,” ASME Journal of Dynamic Systems, Measurement, and Control, vol. 107, no. 1, pp. 1-7, 1985.
[28] R. J. Anderson, and M. W. Spong, “Hybrid Impedance Control of Robotic Manipulators,” IEEE Journal on Robotics and Automation, vol. 4, no. 5, pp. 549-556, 1988.
[29] J. Li, L. Liu, Y. Wang, and W. Liang, “Adaptive Hybrid Impedance Control of Robot Manipulators with Robustness Against Environment's Uncertainties,” in Proceedings of 2015 IEEE International Conference on Mechatronics and Automation (ICMA), 2-5 Aug. 2015, pp. 1846-1851
[30] K. Mouri, K. Terashima, P. Minyong, H. Kitagawa, and T. Miyoshi, “Identification and Hybrid Impedance Control of Human Skin Muscle by Multi-Fingered Robot Hand,” in Proceedings of 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 29 Oct.-2 Nov. 2007, pp. 2895-2900
[31] J. Seul, T. C. Hsia, and R. G. Bonitz, “Force Tracking Impedance Control of Robot Manipulators Under Unknown Environment,” IEEE Transactions on Control Systems Technology, vol. 12, no. 3, pp. 474-483, 2004.
[32] J. Duan, Y. Gan, M. Chen, and X. Dai, “Adaptive Variable Impedance Control for Dynamic Contact Force Tracking in Uncertain Environment,” Robotics and Autonomous Systems, vol. 102, pp. 54-65, 2018.
[33] H. Cao, X. Chen, Y. He, and X. Zhao, “Dynamic Adaptive Hybrid Impedance Control for Dynamic Contact Force Tracking in Uncertain Environments,” IEEE Access, vol. 7, pp. 83162-83174, 2019.
[34] D. Erickson, M. Weber, and I. Sharf, “Contact Stiffness and Damping Estimation for Robotic Systems,” The International Journal of Robotics Research, vol. 22, no. 1, pp. 41-57, 2003.
[35] B. Komati, C. Clevy, and P. Lutz, “Force Tracking Impedance Control with Unknown Environment at the Microscale,” in Proceedings of 2014 IEEE International Conference on Robotics and Automation (ICRA), 31 May-7 June 2014, pp. 5203-5208
[36] 胡智皓，選擇順應性裝配機械手臂之外力估測與順應性研究，碩士論文，國立成功大學電機工程學系，2016。
[37] 莊閔皓，六軸工業用機械手臂之系統鑑別與順應控制研究，碩士論文，國立成功大學電機工程學系，2016。
[38] 高子洋，工業用機械手臂之阻抗控制研究，碩士論文，國立成功大學電機工程學系，2017。
[39] 劉家佑，六軸工業用機械手臂之基於順應控制教導器研究，碩士論文，國立成功大學電機工程學系，2019。
[40] 嚴士翔，基於服務型應用之無感測器機械手臂與安全力量控制器開發，博士論文，國立臺灣科技大學機械工程系，2018。
[41] 藍彥明，自動偵測研磨厚度及力量控制長纖複材研磨機的開發，碩士論文，國立成功大學機械工程學系，2017。
[42] 闕子晨，五自由度機械手臂之開發與力量控制應用，碩士論文，國立臺灣科技大學機械工程系，2017。
[43] 陳仁杰，基於模糊力量控制之六自由度機械手臂的順應性控制，碩士論文，淡江大學電機工程學系碩士班，2016。
[44] 吳秉昇，混合位置/力量控制應用於機械手臂自動化打蠟之研究，碩士論文，國立臺灣科技大學材料科學與工程系，2016。
[45] K. S. Fu, R. C. Gonzalez, and C. S. G. Lee, Robotics: Control, Sensing, Vision, and Intelligence, New York, NY: McGraw-Hill, 1987, pp. 84-96.
[46] J. Swevers, W. Verdonck, and J. De Schutter, “Dynamic Model Identification for Industrial Robots,” IEEE Control Systems Magazine, vol. 27, no. 5, pp. 58-71, Oct. 2007.
[47] C. Ott, Cartesian Impedance Control of Redundant and Flexible-Joint Robots, Tokyo, Springer, Sep. 2008.