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Motion prediction and supervisory control of the macro–micro parallel manipulator system

Published online by Cambridge University Press:  11 April 2011

Xuechao Duan ,
Yuanying Qiu ,
Jianwei Mi  and
Ze Zhao
Xuechao Duan*
Affiliation:
Research Institute on Mechatronics, School of Electromechanical Engineering, Xidian University, Xi'an 710071, P. R. China
Yuanying Qiu
Affiliation:
Research Institute on Mechatronics, School of Electromechanical Engineering, Xidian University, Xi'an 710071, P. R. China
Jianwei Mi
Affiliation:
Research Institute on Mechatronics, School of Electromechanical Engineering, Xidian University, Xi'an 710071, P. R. China
Ze Zhao
Affiliation:
Research Institute on Mechatronics, School of Electromechanical Engineering, Xidian University, Xi'an 710071, P. R. China
*
*Corresponding author. E-mail: xchduan@xidian.edu.cn
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Summary

This paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a canonical PID controller is discussed. In addition, the digital servo filters with feedforward are employed in the linear actuators as the lower layer controllers. Experimental results of a one-tenth scale FAST field model validate the effectiveness of the supervisory controller and the motion prediction algorithm.

Keywords

Macro–micro parallel manipulatorSupervisory controlTracking and predictionAdaptive interactionForward position kinematicsStewart platform
Type
Articles
Information
Robotica , Volume 29 , Issue 7 , December 2011 , pp. 1005 - 1015
Copyright
Copyright © Cambridge University Press 2011

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References

1.Nan, R., “Five hundred meter aperture spherical radio telescope (FAST),” Sci. China, Ser. G: Phys. Astron. 49 (2), 129–148 (2006).CrossRefGoogle Scholar
2.Castleberg, P. A. and Xilouris, K. M., “Arecibo observatory,” IEEE Potentials 16 (3), 33–35 (1997).CrossRefGoogle Scholar
3.Love, A. W., “Arecibo observatory 40th anniversary celebration,” IEEE Potentials 23 (2), 41–44 (2004).CrossRefGoogle Scholar
4.Duan, B. Y., “A new design project of the line feed structure for large radio telescope and its nonlinear dynamic analysis,” Mechatronics 9 (1), 53–64 (1999).CrossRefGoogle Scholar
5.Zi, B., Duan, B. Y., Du, J. L. and Bao, H., “Dynamic modeling and active control of a cable-suspended parallel robot,” Mechatronics 18 (1), 1–12 (2008).CrossRefGoogle Scholar
6.George, L. E., Active Vibration Control of a Flexible Base Manipulator PhD Dissertation (Atlanta, GA: Georgia Institute of Technology, 2002).CrossRefGoogle Scholar
7.Lin, J. and Huang, Z. Z., “A novel PID control parameters tuning approach for robot manipulators mounted on oscillatory bases,” Robotica 25 (4), 467–477 (2007).CrossRefGoogle Scholar
8.Lew, J. Y. and Moon, S. M., “A simple active damping control for compliant base manipulators,” IEEE/ASME Trans. Mechatronics 6 (3), 305–310 (2001).CrossRefGoogle Scholar
9.Begovich, O., Sanchez, E. N. and Maldonado, M., “Takagi-Sugeno fuzzy scheme for real-time trajectory tracking of an underactuated robot,” IEEE Trans. Control Syst. Technol. 10 (1), 14–20 (2002).CrossRefGoogle Scholar
10.Dasgupta, B. and Mruthyunjaya, T. S., “A Newton-Euler formulation for the inverse dynamics of the Stewart platform manipulator,” Mech. Mach. Theory 33 (8), 1135–1152 (1998).CrossRefGoogle Scholar
11.Fu, S., Yao, Y. and Wu, Y. et al. , “Comments on “A Newton-Euler formulation for the inverse dynamics of the Stewart platform manipulator by B. Dasgupta and T. S. Mruthyunjaya [Mech. Mach. Theory 33, 1135–1152 (1998)],” Mech. Mach. Theory 42(12), 1668–1671 (2007).Google Scholar
12.Sharon, A., Hogan, N. and Hardt, D. E., “The macro/micro manipulator: An improved architecture for robot control,” Robot. Comput–Integr. Manuf. 10 (3), 209–222 (1993).CrossRefGoogle Scholar
13.Magee, D. P. and Book, W. J., “Filtering Micro-Manipulator Wrist Commands to Prevent Flexible Base Motion,” Proceedings of Proceedings of the 1995 American Control Conference. Part 1 (of 6), Seattle, WA, USA (Jun. 21–23, 1995) pp. 924–928.CrossRefGoogle Scholar
14.Lew, J. Y. and Trudnowski, D. J., “Vibration control of a micro/macro-manipulator system,” IEEE Control Syst. Mag. 16 (1), 26–31 (1996).Google Scholar
15.Yim, W. and Sahjendra, N. S.. “Nonlinear Inverse and Predictive End Point Trajectory Control of Flexible Macro-Micro Manipulators,” Proceedings of the 13th World Congress of IFAC. Vol. A: Robotics, Components and Instruments, San Francisco (1996) pp. 97–102.Google Scholar
16.Sharf, I., “Active Damping of a Large Flexible Manipulator with a Short-Reach Robot,” Proceedings of the 1995 American Control Conference. Part 1 (of 6), Seattle, WA, USA (Jun. 21–23, 1995) pp. 3329–3333.CrossRefGoogle Scholar
17.Cheng, X. P. and Patel, R. V., “Neural network based tracking control of a flexible macro-micro manipulator system,” Neural Netw. 16 (2), 271–286 (2003).CrossRefGoogle ScholarPubMed
18.Bassan, H., Talebi, H. A., Patel, R. V. and Moallem, M. “Control of a rigid manipulator mounted on a compliant base,” Robotica 23 (2), 197–206 (2005).CrossRefGoogle Scholar
19.Mannani, A. and Talebi, H. A., “A fuzzy Lyapunov-based control strategy for a macro-micro manipulator: Experimental results,” IEEE Trans. Control Syst. Technol. 15 (2), 375–383 (2007).CrossRefGoogle Scholar
20.Duan, B. Y., Qiu, Y. Y. and Zhang, F. S. et al. , “On design and experiment of the feed cable-suspended structure for super antenna,” Mechatronics 19 (4), 503–509 (2009).CrossRefGoogle Scholar
21.Li, T., Zheng, H., Wang, J. and Duan, G., “Precision measures for various configuration of parallel kinematic machine tools,” Chinese J. Mech. Eng. 38 (9), 101–105 (2002).CrossRefGoogle Scholar
22.Duan, X. C., Qiu, Y. Y. and Duan, B. Y., “Analysis of servo bandwidth of the fine tuning Stewart platform for the large radio telescope,” Zhongguo Jixie Gongcheng/China Mech. Engng 16 (3), 245–248 (2005).Google Scholar
23.Han, J., Active Disturbance Rejection Control Technique-The Technique for Estimating and Compensating the Uncertainties (National Defense Industry Press, Beijing, 2008).Google Scholar
24.Han, J., “Active disturbances rejection control technique,” Frontier Sci. 1 (1), 24–31 (2007).Google Scholar
25.Wang, Y., “A direct numerical solution to forward kinematics of general Stewart-Gough platforms,” Robotica 25 (1), 121–128 (2007).CrossRefGoogle Scholar
26.Yuan, Y. and Sun, W., Optimized Theory and Methods (Science Press, Beijing, 2003).Google Scholar
27.Brandt, R. D. and Lin, F., “Supervised learning in neural networks without feedback network,” Proceedings of the 1996 IEEE International Symposium on Intelligent Control, Dearborn, MI, USA (Sep. 15–18, 1996) pp. 86–90.CrossRefGoogle Scholar
28.Lin, F., Brandt, R. D. and Saikalis, G., “Self-tuning of PID controllers by adaptive interaction,” Proceedings of American Control Conference, Chicago, IL, USA (Jun. 28–30, 2000) pp. 3676–3680.Google Scholar