Abstract
A robust neuro-adaptive controller for uncertain flexible joint robots is presented. This control scheme integrates H-infinity disturbance attenuation design and recurrent neural network adaptive control technique into the dynamic surface control framework. Two recurrent neural networks are used to adaptively learn the uncertain functions in a flexible joint robot. Then, the effects of approximation error and filter error on the tracking performance are attenuated to a prescribed level by the embedded H-infinity controller, so that the desired H-infinity tracking performance can be achieved. Finally, simulation results verify the effectiveness of the proposed control scheme.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
K. Khorasani. Nonlinear feedback control of flexible joint manipulators: a single link case study. IEEE Transactions on Automatic Control, 1990, 35(10): 1145–1149.
M. W. Spong, M. V. Vidyasager. Robot Dynamics and Control. Singapore: John Wiley, 1989: 281–295.
F. Ghorbel, J. Hung, M. W. Spong. Adaptive control of flexible-joint manipulators. IEEE Control System Magazine, 1989, 9(7): 9–13.
M. W. Spong, K. Khorasani, P. V. Koktovic. An integral manifold approach to the feedback control of flexible-joint robots. IEEE Robotics and Automation, 1987, 3(4): 291–300.
F. Ghorbel, M. W. Spong. Adaptive integral manifold control of flexible joint robot manipulators. Proceedings of IEEE Internatinal Conference on Robotics and Automation. New York: IEEE, 1992: 707–714.
D. G. Taylor. Composite control of direct-drive robots. IEEE Proceedings of the 28th Conference on Decision and Control. New York: IEEE, 1989: 1670–1675.
A. A. Abouelsoud. Robust regulator for flexible-joint robots using integrator backstepping. Journal of Intelligent and Robotic Systems, 1998, 22(1): 23–38.
H. O. Jong, J. S. Lee. Control of flexible joint robot system by backstepping design approach. Proceedings of the IEEE International Conference on Robotics and Automation. New York: IEEE, 1997: 3435–3440.
M. M. Bridges, D. M. Dawson, C. T. Abdallah. Control of rigid-link flexible-joint robots: a survey of backstepping approaches. Journal Robotic Systems, 1995, 12(3): 199–216.
C. A. Lightcap, S. A. Banks. An extended Kalman filter for real-time estimation and control of a rigid-link flexible-joint manipulator. IEEE Transactions on Control Systems Technology, 2010, 18(1): 91–103.
C. M. Kwan, F. L. Lewis, Y. H. Kim. Robust neural network control of rigid link flexible joint robots. Asian Journal of Control, 1999, 1(3): 188–197.
C. M. Kwan, F. L. Lewis. Robust backstepping control of nonlinear systems using neural networks. IEEE Transactions on Systems, Man, and Cybernetics — Part A: Systems and Humans, 2000, 30(6): 753–766.
T. Zhang, S. S. Ge, C. Hang. Adaptive neural network control for strict-feedback nonlinear systems using backstepping design. Automatica, 2000, 36(12): 1835–1846.
S. S. Ge, C. Wang. Direct adaptive NN control of a class of nonlinear systems. IEEE Transactions on Neural Networks, 2002, 13(1): 214–221.
Y. Li, S. Qiang, X. Zhuang, et al. Robust and adaptive backstepping control for nonlinear systems using RBF neural networks. IEEE Transactions on Neural Networks, 2004, 15(3): 693–701.
D. Swaroop, J. C. Gerdes, P. P. Yip, et al. Dynamic surface control of nonlinear systems. Proceedings of the American Control Conference. New York: IEEE, 1997: 3028–3034.
D. Swaroop, J. K. Hedrick, P. P. Yip, et al. Dynamic surface control for a class of nonlinear systems. IEEE Transactions on Automatic Control, 2000, 45(10): 1893–1899.
D. Wang, J. Huang. Neural network-based adaptive dynamic surface control for a class of uncertain nonlinear systems in strict-feedback form. IEEE Transactions on Neural Networks, 2005, 16(1): 195–202.
W. Chen. Adaptive backstepping dynamic surface control for systems with periodic disturbances using neural networks. IET Control Theory and Applications, 2009, 3(10): 1383–1394.
S. J. Yoo, J. B. Park, Y. H. Choi. Adaptive dynamic surface control of flexible-joint robots using self-recurrent wavelet neural networks. IEEE Transactions on Systems, Man, and Cybernetics — Part B: Cybernetics, 2006, 36(6): 1342–1355.
S. J. Yoo, J. B. Park, Y. H. Choi. Adaptive output feedback control of flexible-joint robots using neural networks: dynamic surface design approach. IEEE Transactions on Neural Networks, 2008, 19(10): 1712–1726.
S. S. Ge, C. Wang. Adaptive neural control of uncertain MIMO nonlinear systems. IEEE Transactions on Neural Networks, 2004, 15(3): 674–692.
C. Lin, C. F. Hsu. Recurrent neural network based adaptive backstepping control for induction servo motors. IEEE Transactions on Industrial Elctronics, 2005, 52(6): 1677–1684.
C. Lin, W. D. Chou, F. Lin. Adaptive hybrid control using a recurrent neural network for a linear synchronous motor servo-drive system. IEE Proceedings — Control Theory and Applications, 2001, 148(2): 156–168.
F. Lin, R. Wai, W. Chou, et al. Adaptive backstepping control using recurrent neural network for linear induction motor drive. IEEE Transactions on Industrial Electronics, 2002, 49(1): 134–146.
C. Ku, K. Y. Lee. Diagonal recurrent neural networks for dynamic systems control. IEEE Transactions on Neural Networks, 1995, 6(1): 144–156.
C. Lin, C. Chen. CMAC-based supervisory control for nonlinear chaotic systems. Chaos, Solitons and Fractals, 2008, 35(1): 40–58.
C. Lin, Y. Peng, C. F. Hsu. Robust cerebellar model articulation controller design for unknown nonlinear systems. IEEE Transactions on Circuits and Systems — II: Express Briefs, 2004, 51(7): 354–358.
F. Lin, T. S. Lee, C. Lin. Robust controller design with recurrent neural network for linear synchronous motor drive. IEEE Transactions on Industrial Electronics, 2003, 50(3): 456–470.
C. Chen, C. Lin, T. Chen. Intelligent adaptive control for MIMO uncertain nonlinear systems. Expert Systems with Applications, 2008, 35(3): 865–877.
C. F. Hsu, C. Lin, T. T. Lee. Wavelet adaptive backstepping control for a class of nonlinear systems. IEEE Transactions on Neural Networks, 2006, 17(5): 1175–1183.
M. C. Hwang, X. Hu, Y. Shrivastava. Adaptive neural network tracking controller for electrically driven manipulators. IEE Proceedings — Control Theory and Applications, 1998, 145(6): 594–602.
S. J. Yoo, J. B. Park. Practical robust control for flexible joint robot manipulators. IEEE International Conference on Robotics and Automation. New York: IEEE, 2008: 3377–3382.
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China (Nos. 60835004, 61175075), and the Hunan Provincial Innovation Foundation for Postgraduate (No. CX2012B147).
Zhiqiang MIAO received his B.S. and M.S. degrees in Electrical and Information Engineering from Hunan University, Changsha, China, in 2010 and 2012, respectively, where he is currently working toward the Ph.D. degree with the College of Electrical and Information Engineering. His research interests include intelligent control theory and application and robot control.
Yaonan WANG received his B.S. degree in Computer Engineering from East China Science and Technology University (ECSTU), Fuzhou, China, in 1981, and M.S. and Ph.D. degrees in Electrical Engineering from Hunan University, Changsha, China, in 1990 and 1994, respectively. From 1994 to 1995, he was a postdoctoral research fellow with the National University of Defence Technology. From 1981 to 1994, he worked with ECSTU. From 1998 to 2000, he was a senior Humboldt fellow in Germany, and from 2001 to 2004, he was a visiting professor with the University of Bremen, Bremen, Germany. He has been a professor at Hunan University since 1995. His research interests include intelligent control and information processing, robot control, industrial process control, and image processing.
Rights and permissions
About this article
Cite this article
Miao, Z., Wang, Y. Robust dynamic surface control of flexible joint robots using recurrent neural networks. J. Control Theory Appl. 11, 222–229 (2013). https://doi.org/10.1007/s11768-013-1240-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11768-013-1240-x