Iterative learning control with sampled-data feedback for robot manipulators

• Autorzy: Delchev K. , Boiadjiev G. , Kawasaki H. , Mouri T.

Identyfikatory

DOI

10.2478/acsc-2014-0018

• Języki publikacji: EN

Abstrakty

EN

This paper deals with the improvement of the stability of sampled-data (SD) feedback control for nonlinear multiple-input multiple-output time varying systems, such as robotic manipulators, by incorporating an off-line model based nonlinear iterative learning controller. The proposed scheme of nonlinear iterative learning control (NILC) with SD feedback is applicable to a large class of robots because the sampled-data feedback is required for model based feedback controllers, especially for robotic manipulators with complicated dynamics (6 or 7 DOF, or more), while the feedforward control from the off-line iterative learning controller should be assumed as a continuous one. The robustness and convergence of the proposed NILC law with SD feedback is proven, and the derived sufficient condition for convergence is the same as the condition for a NILC with a continuous feedback control input. With respect to the presented NILC algorithm applied to a virtual PUMA 560 robot, simulation results are presented in order to verify convergence and applicability of the proposed learning controller with SD feedback controller attached.

Słowa kluczowe

EN

sampled-data systems; iterative learning control; robot manipulators; convergence analysis

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2014
• Tom: Vol. 24, no. 3
• Strony: 299--319
• Opis fizyczny: Bibliogr. 21 poz., rys., tab.

Twórcy

autor

Delchev K.

• Institute of Mechanics, Bulgarian Academy of Sciences, "Acad.G.Bonchev" Str., bl.4, BG-1113 Sofia

autor

Boiadjiev G.

• Faculty of Mathematics and Informatics, Sofia University

autor

Kawasaki H.

• Department of Human and Information Systems, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan

autor

Mouri T.

• Department of Human and Information Systems, Gifu University, 1-1 Yanagido, Gifu, 501-1193, Japan

Bibliografia

• [1] A. M. Pertew, H. J. Marquez and Q. Zhao: A direct sampled-data design approach for robot stabilization.Proc. of the 2008 American Control Conference, Seattle, Washington, (2008), 369-374.
• [2] T. Oomen, J. Van De Wijdeven and Q. Zhao; Suppressing intersample behavior in iterative learning control. Automatica 45 (2009), 981-988.
• [3] Y.-J. Pan, H. J. Marquez and T. Chen: Sampled-data iterative learning control for a class of nonlinear networked control systems. Proc. of the 2006 American Control Conference, Minneapolis, Minnesota, (2006), 3494-3499.
• [4] T.-W. Ma and H. Yang: Sampled data feedback-feedforward control of structures with time delays. J. of Structural Engineering, 132(7), (2006), 1129-1138.
• [5] S. Arimoto, S. Kawamura and F. Miyazaki: Bettering operation of dynamic systems by learning: A new control theory for servomechanism of mechatronics systems. Proc. 23rd Conf. on Decision and Control, Las Vegas, NV, (1984), 1064-1068.
• [6] H.-S. Ahn, K. Moore and Y. Chen: Iterative Learning Control Robustness and Monotonic Convergence for Interval Systems. Springer-Verlag London Limited, London, 2007.
• [7] J.-X. Xu and Y. Tan: Linear and Nonlinear Iterative Learning Control, Lecture Notes in Control and Information Sciences. Springer, New York, 2003.
• [8] H.-S. Ahn and D. A. Bristow: Special Issue on ‘Iterative Learning Control’, Asian J. of Control, 13(1), (2011), 1-2.
• [9] K. Delchev and E. Zahariev: Computer simulation based synthesis of learning control law of robots. Mechanics Based Design of Structures and Machines, 36(3), (2008), 225-248.
• [10] K. Delchev: Iterative learning control for nonlinear systems: A bounded-error algorithm. Asian J. of Control, 15(2), (2013), 453-460.
• [11] B. Zhang, D. Wang, Y. Wang, Y. Ye and K. Zhou: Comparison studies on anti-aliasing/anti-imaging filtering and signal extension in multi-rate ILC. Proc. 17th World Congress of The International Federation of Automatic Control, Seoul, (2008), 12468-12473.
• [12] D. Vassileva, G. Boiadjiev, H. Kawasaki and T. Mouri: Force compensating trajectories for redundant robots: Experimental results. J. of Robotics and Mechatronics, 21(1), (2009), 104-112.
• [13] V. Kozlov, V. Makarychev, A. Timofeev and E. Yurevich: Dynamics of Robot Control, Nauka, Moscow, 1984, (in Russian).
• [14] J. Zhang: Advanced Pulse Width Modulation Controller ICs for Buck DC-DC Converters. Ph.D. Thesis, University of California, Berkeley, 2006.
• [15] R. W. Longman: Iterative learning control and repetitive control for engineering practice. Int. J. of Control, 73(10), (2000), 930-954.
• [16] D. Heinzinger, B. Fenwick, B. Paden and F. Miyazaki: Robust learning control. Proc. 28th Conf. on Decision and Control, Tampa, FL, (1989), 436-440.
• [17] D. Heinzinger, B. Fenwick, B. Paden and F. Miyazaki: Stability of learning control with disturbances and uncertain initial conditions. IEEE Trans. on Automatic Control, 37(1), (1992), 110-114.
• [18] R. James and G. James: Mathematics Dictionary. 5th ed., Chapman & Hall, New York, 1992.
• [19] B. Armstrong, O. Khatib and J. Burdick: The explicit dynamic model and inertial parameters of the PUMA 560 arm. Proc. IEEE Int. Conf. Robotics and Automation, San Francisco, USA, 1 (1986), 510-518.
• [20] P. Corke and B. Armstrong-Helouvry: A search for consensus among model parameters reported for the PUMA 560 robot. Proc. IEEE Int. Conf. Robotics and Automation, San Diego, USA, 1 (1994), 1608-1613.
• [21] T. Tarn, K. Bejczy, S. Han and X. Yun: Inertia parameters of Puma 560 robot arm. Tech. Rep. SSM-RL-85-01, Washington University, St. Louis, MO, 1985.