不确定机器人的力/位置智能控制
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摘要
近几十年来，国际上对机器人的控制问题进行了大量研究，尤其是机器人的力/位置控制问题吸引了许多学者和专家的注意。许多学者不断运用新的控制理论和方法，从不同的角度，对机器人的力/位置控制进行理论和实际应用上的尝试。然而，机器人本身是一种高度非线性、强耦合且含有诸多不确定性因素的对象，当机器人的末端执行器与外界环境接触时，工作环境接触刚度的不同对控制性能也有较大的影响，机器人的应用范围因而受到极大制约。
基于上述问题，本文在力/位置控制的基础上，主要针对机器人参数摄动及外界工作环境接触刚度的不确定进行研究。
在机器人力/位置混合控制的基础上，首先设计了一种模糊神经网络控制器与反馈控制器相结合的控制方案，采用模糊神经网络在线学习所有不确定性的包络函数的上界，引入反馈控制器，以增强模糊神经网络控制策略的完备性。针对实际应用中对不确定的不同要求，又提出了一种具有混合/性能指标的神经网络控制方法，对机器人不确定性(包括参数不确定性和外部扰动)分别进行补偿，保证了系统对外界干扰在给定的干扰衰减度下具有鲁棒稳定性的同时，还增强了系统对参数不确定性的补偿。
力/位置混合控制理论明确，但付诸实施难。为此，本文又在机器人阻抗控制的基础上，针对机器人和环境的不确定性，提出一种具有鲁棒性的阻抗控制结构，使用模糊神经网络作为补偿控制器消除力控制中的所有不确定性，具有较强的鲁棒性和较好的实用价值。最后，针对阻抗控制对期望力的跟踪能力，提出一种自适应力跟踪策略，它根据力误差修正参考位置，不需要知道环境位置和刚度的先验知识，并且对补偿不确定性有一定的鲁棒性，但计算量较大。在此基础上，又提出一种以位置控制为基础的模糊神经网络阻抗控制方案，计算量小，达到了理想的力跟踪效果。
In the recent decades, many efforts have been devoted to the control of robot manipulators. Especially, problems of free motion and constrained motion for robot manipulators have attached many researchers and experts. Many researchers try many new theory and ways to robot force/position control from different point of view. It is know that there exist complex nonlinear, strong coupling and lots of uncertainties in robotic system, and when the manipulator end-effector contacts with the environment, the different environment stiffness have great affection on the system’s performance. Hence, robot’s applying scope is greatly constricted.
In this dissertation, parameter perturbation and the uncertainties of environment stiffness are mainly studied based on robot force/position control.
A new control strategy is presented by combining fuzzy-neural control with feedback control under considering the uncertainties of robotic system firstly based on robot force/position hybrid control. Fuzzy-neural network is used to learning the boundary of envelope function of uncertainties, and the feedback controller is used to enhance the complete performance of fuzzy-neural control strategy. After that, a fuzzy neural network control design with mixed performance was proposed aiming at different requirement to uncertainty, which separately compensate parameter uncertainties and external disturbances of robot system. This method can ensure the robust stability that under a prescribed attenuation level for the external disturbance, and also strengthens compensation to parameter uncertainties.
The theory of hybrid force/position control is clear, however, it is difficult to implement. Then an impedance control strategy with robust performance is presented aiming at uncertainties of robot, FNN is used to learning the uncertainties in order to eliminate disturbance, which have good robust and high value in practice. Finally, an adaptive method is presented
aiming at the tracking ability of impedance control. It modifies the reference position according to the force error on line so that the prior knowledge of the environment stiffness and location is not required. And it is robust to compensate uncertainties, but have long computation time. Then, a fuzzy neural controller is presented based on position control, which have short computation time and can successfully tracking reference force.
基于上述问题，本文在力/位置控制的基础上，主要针对机器人参数摄动及外界工作环境接触刚度的不确定进行研究。
在机器人力/位置混合控制的基础上，首先设计了一种模糊神经网络控制器与反馈控制器相结合的控制方案，采用模糊神经网络在线学习所有不确定性的包络函数的上界，引入反馈控制器，以增强模糊神经网络控制策略的完备性。针对实际应用中对不确定的不同要求，又提出了一种具有混合/性能指标的神经网络控制方法，对机器人不确定性(包括参数不确定性和外部扰动)分别进行补偿，保证了系统对外界干扰在给定的干扰衰减度下具有鲁棒稳定性的同时，还增强了系统对参数不确定性的补偿。
力/位置混合控制理论明确，但付诸实施难。为此，本文又在机器人阻抗控制的基础上，针对机器人和环境的不确定性，提出一种具有鲁棒性的阻抗控制结构，使用模糊神经网络作为补偿控制器消除力控制中的所有不确定性，具有较强的鲁棒性和较好的实用价值。最后，针对阻抗控制对期望力的跟踪能力，提出一种自适应力跟踪策略，它根据力误差修正参考位置，不需要知道环境位置和刚度的先验知识，并且对补偿不确定性有一定的鲁棒性，但计算量较大。在此基础上，又提出一种以位置控制为基础的模糊神经网络阻抗控制方案，计算量小，达到了理想的力跟踪效果。
In the recent decades, many efforts have been devoted to the control of robot manipulators. Especially, problems of free motion and constrained motion for robot manipulators have attached many researchers and experts. Many researchers try many new theory and ways to robot force/position control from different point of view. It is know that there exist complex nonlinear, strong coupling and lots of uncertainties in robotic system, and when the manipulator end-effector contacts with the environment, the different environment stiffness have great affection on the system’s performance. Hence, robot’s applying scope is greatly constricted.
In this dissertation, parameter perturbation and the uncertainties of environment stiffness are mainly studied based on robot force/position control.
A new control strategy is presented by combining fuzzy-neural control with feedback control under considering the uncertainties of robotic system firstly based on robot force/position hybrid control. Fuzzy-neural network is used to learning the boundary of envelope function of uncertainties, and the feedback controller is used to enhance the complete performance of fuzzy-neural control strategy. After that, a fuzzy neural network control design with mixed performance was proposed aiming at different requirement to uncertainty, which separately compensate parameter uncertainties and external disturbances of robot system. This method can ensure the robust stability that under a prescribed attenuation level for the external disturbance, and also strengthens compensation to parameter uncertainties.
The theory of hybrid force/position control is clear, however, it is difficult to implement. Then an impedance control strategy with robust performance is presented aiming at uncertainties of robot, FNN is used to learning the uncertainties in order to eliminate disturbance, which have good robust and high value in practice. Finally, an adaptive method is presented
aiming at the tracking ability of impedance control. It modifies the reference position according to the force error on line so that the prior knowledge of the environment stiffness and location is not required. And it is robust to compensate uncertainties, but have long computation time. Then, a fuzzy neural controller is presented based on position control, which have short computation time and can successfully tracking reference force.
引文
蔡自兴. 机器人学. 北京:清华大学出版社, 2000
熊有伦. 机器人学. 北京:机械工业出版社, 1993
Kiguchi, Kazuo and Watanabe. Two-Stage Adaptive Robot Position/Force Control Using Fuzzy Reasoning and Neural Networks. Advanced Robotics, 2000,14(3): 153-168
J. J. Craig. Adaptive Control of Mechanical Manipulators. The International Journal of Robotics Research, 1987, 6(2):172-184
B. Paden, R. Panja. Globally Asymptotically ‘PD+’ Controller for Rigid Robot Manipulators. Int. J. Control, 1988, 47(6):1697-1712
Niu Yugang, Yang Chengwu. An Adaptive Neural Robust Controller for Trajectory
of Robot Manipulators. Control Theory and Applications, 2000, 17(6):924-928
B. Yao, M. Tomizuka. Comparative Experiments of Robust and Adaptive Control with New Robust Adaptive Controller for Robot Manipulators. Proc. of The IEEE Conference on Decision and Control, 1994, (2):1290-1295
Villani, Luigi and N. Ciro. Experimental Study of Adaptive Force/Position Control Algorithms of Industrial Robot. IEEE Transactions on Control Systems Technology 2000, (9):777-786
Hsu. feng-Yih, F. L. Chen. New Adaptive Fuzzy Hybrid Force/Position Control for Intelligent Robot Debarring. Proceedings IEEE International Conference on Robotics and Automation, 1999, 3(5):2476-2481
T. C. Hisa. Adaptive Control of Robot Manipulator Review. IEEE Int. Conf. Robotic and Automation, 1986, (9):652-663
E. M. Joo, G. Yang. Robust Adaptive Control of Robot Manipulators Using Generalized Fuzzy Neural Networks. IEEE Transactions on Industrial Electronics, 2003, 50(6):620-628
G. W. Zeng, Ahmad Hemami. An Overview of Robot Force Control. Robotic, 1997, 15(5):473-482
A. Andrew, Goldenberg and Peilin Song. Principles for Design of Position and Force Controllers for Manipulators. Robotics and Autonomous Systems, 1977, (3):263-277
T. Yoshikawa, T. Sugie and M. Tanaka. Dynamic Hybrid Position/Force Control of Robot Manipulator Constrained A Stiff Environment. IEEE Robotic and Automation, 1988, 4(6):321-333
Homayoun Seraji, Richard Colbaugh. Force Tracking Impedance Control. The International Journal of Robotics Research, 1997, 16(2):97-117
Lucibello, Pasquale. Learning Algorithm for Impendence Hybrid Force Position Control of Robot Arms. IEEE Transactions on Systems, Man, and Cybernetics, 1998, 28(5):241-244
N. Hogan. Impedance Control: An Approach to Manipulation: Part I-theory. Part II-Implementation. Part III-Applications. J Dyn Sys Meas Cont, 1985:1-24
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A. Hace, K. Jezernik and S. Uran. Robust Impendence Control. IEEE Conference on Control Applications-Proceedings, 1998, 1(9):583-587
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C. M. Kwan. Robust Force/Motion Control of Constrained Robots Using Neural Network. Proceedings of The 33rd Conference on Decision and Control, 1944, (12): 1862-1867
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J. S. Reed, P. Ioannou. Instability Analysis and Robust Adaptive Control of Robotic Manipulators. IEEE Transactions on Robotics and Automation, 1989, 5(3):381-386
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Connolly, Thom and H. Pfeiffer Friedrich. Neural Network Hybrid Position/force Control. IEEE International Conference on Intelligent Robots and Systems, 1993, (9):240-244
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Jung, Seul and T. C. Hsia. Robust Neural Force Control Scheme under Uncertainties in Robot Dynamics and Unknown Environment. IEEE Transactions on Industrial Electronics, 2000, 47(2):403-412
R. J. Wai, Hsieh and K. Yun. Robust Fuzzy Neural Network Control for n-link Robot Manipulator. Journal of The Chinese Institute of Electrical Engineering, Transactions of The Chinese Institute of Engineers, Series E/Chung Kuo Tien Chi Kung Chieng Hsueh K’an, 2003, 10(2):21-32
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Kiguchi, Kazuo and Watanabe. Two-Stage Adaptive Robot Position/Force Control Using Fuzzy Reasoning and Neural Networks. Advanced Robotics, 2000,14(3): 153-168
J. J. Craig. Adaptive Control of Mechanical Manipulators. The International Journal of Robotics Research, 1987, 6(2):172-184
B. Paden, R. Panja. Globally Asymptotically ‘PD+’ Controller for Rigid Robot Manipulators. Int. J. Control, 1988, 47(6):1697-1712
Niu Yugang, Yang Chengwu. An Adaptive Neural Robust Controller for Trajectory
of Robot Manipulators. Control Theory and Applications, 2000, 17(6):924-928
B. Yao, M. Tomizuka. Comparative Experiments of Robust and Adaptive Control with New Robust Adaptive Controller for Robot Manipulators. Proc. of The IEEE Conference on Decision and Control, 1994, (2):1290-1295
Villani, Luigi and N. Ciro. Experimental Study of Adaptive Force/Position Control Algorithms of Industrial Robot. IEEE Transactions on Control Systems Technology 2000, (9):777-786
Hsu. feng-Yih, F. L. Chen. New Adaptive Fuzzy Hybrid Force/Position Control for Intelligent Robot Debarring. Proceedings IEEE International Conference on Robotics and Automation, 1999, 3(5):2476-2481
T. C. Hisa. Adaptive Control of Robot Manipulator Review. IEEE Int. Conf. Robotic and Automation, 1986, (9):652-663
E. M. Joo, G. Yang. Robust Adaptive Control of Robot Manipulators Using Generalized Fuzzy Neural Networks. IEEE Transactions on Industrial Electronics, 2003, 50(6):620-628
G. W. Zeng, Ahmad Hemami. An Overview of Robot Force Control. Robotic, 1997, 15(5):473-482
A. Andrew, Goldenberg and Peilin Song. Principles for Design of Position and Force Controllers for Manipulators. Robotics and Autonomous Systems, 1977, (3):263-277
T. Yoshikawa, T. Sugie and M. Tanaka. Dynamic Hybrid Position/Force Control of Robot Manipulator Constrained A Stiff Environment. IEEE Robotic and Automation, 1988, 4(6):321-333
Homayoun Seraji, Richard Colbaugh. Force Tracking Impedance Control. The International Journal of Robotics Research, 1997, 16(2):97-117
Lucibello, Pasquale. Learning Algorithm for Impendence Hybrid Force Position Control of Robot Arms. IEEE Transactions on Systems, Man, and Cybernetics, 1998, 28(5):241-244
N. Hogan. Impedance Control: An Approach to Manipulation: Part I-theory. Part II-Implementation. Part III-Applications. J Dyn Sys Meas Cont, 1985:1-24
S. P. Chan, B. Yao and W. B. Gao. Robust Impedance Control of Robot Manipulators. Int J Robotice Automation, 1991, 6(4):220-227
A. Hace, K. Jezernik and S. Uran. Robust Impendence Control. IEEE Conference on Control Applications-Proceedings, 1998, 1(9):583-587
H. Berghuis. A Robust Adaptive Robot Controller. IEEE Transaction on Robotics and Automation, 1993, 9(6):825-830
C. M. Kwan. Robust Force/Motion Control of Constrained Robots Using Neural Network. Proceedings of The 33rd Conference on Decision and Control, 1944, (12): 1862-1867
H. G. Sage, E. Ostertag. Robust Control of Robot Manipulators A Survey. Int. Control, 1999, 72(16):1498-1552
G. Tao. Robust Adaptive Control of Robot Manipulators. Robotics and Automation, 2000, 28(4):803-807
J. S. Reed, P. Ioannou. Instability Analysis and Robust Adaptive Control of Robotic Manipulators. IEEE Transactions on Robotics and Automation, 1989, 5(3):381-386
B. Yao, M. Tomizuka. Robust Adaptive Motion and Force Control of Robot Manipulators in Unknown Stiffness Environment. Proc.32nd IEEE Conf. Decision Contr, 1993, (3):142-147
C. H. Pyung, D. S. Kim and L. W. Jeong. Intelligent Force/Position Control of Robot Manipulator Using Time Delay Control. IEEE/RSJ/GI International Conference on Intelligent Robots and Systems, 1994, (3):1632-1638
Connolly, Thom and H. Pfeiffer Friedrich. Neural Network Hybrid Position/force Control. IEEE International Conference on Intelligent Robots and Systems, 1993, (9):240-244
Y. S. Xu, P. P. Richard and Shum Heung-Yeung. Fuzzy Control of Robot and Compliant Wrist System. IEEE Industry Application Society Annual Meeting, 1991, (2):1431-1437
Tokita. Masatoshi, Mituoka. Toyokazu. Force Control of A Robotic Manipulator by Application of A Neural Network. Advanced Robotics, 1991, 5(1):15-24
F. Toshio, K. Takashi and T. Masatoshi. Position/Force Hybrid Control of Robotic Manipulator by Neural Network (Int. Report: Application of Neural Servo Controller to Stabbling Control). Nippon KikaiGakkaiRonbunshu, C Hen/Transactions of The Japan Society of Mechanical Engineers:Part C, 1990, 56(527):1854-1860
Jung, Seul and T. C. Hsia. Robust Neural Force Control Scheme under Uncertainties in Robot Dynamics and Unknown Environment. IEEE Transactions on Industrial Electronics, 2000, 47(2):403-412
R. J. Wai, Hsieh and K. Yun. Robust Fuzzy Neural Network Control for n-link Robot Manipulator. Journal of The Chinese Institute of Electrical Engineering, Transactions of The Chinese Institute of Engineers, Series E/Chung Kuo Tien Chi Kung Chieng Hsueh K’an, 2003, 10(2):21-32
L. Cai, A. A. Goldenberg. Robust Control of Position and Forces for A Robot Manipulator in Non-contact and Contact Tasks. Proceedings of American Control Conference, 1989:1905~1911
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