Abstract
An iterative learning controller based on experience database is proposed for a class of robotic trajectory tracking tasks. It is very general for supporting all types of iterative learning control schemes. The experience database consists of previously tracked trajectories and their corresponding control inputs. The initial control input of an iterative learning controller can be selected properly using a dynamic RBF neural network by properly considering the past experience of tracking various trajectories. Moreover, the RBF network can be created dynamically to ensure the network size is economical. Simulation results of trajectory tracking of a planar two-link manipulator indicate that the convergence speed of the iterative learning controller can be improved by using this method.
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References
Hanai, A., Choi, H.T., Choi, S.K., Yuh, J.: Minimum Energy Based Fine Motion Control of Underwater Robots in the Presence of Thruster Nonlinearity. In: Proceedings of IEEE International Conference on Intelligent Robots and Systems, pp. 559–564 (2003)
Lee, S.H., Song, J.B., Choi, W.C., Hong, D.: Position Control of a Stewart Platform Using Inverse Dynamics Control with Approximate Dynamics. Mechatronics 13(6), 605–619 (2003)
Yagiz, N.: Robust Control of a Spatial Robot Using Sliding Modes. Mathematical & Computational Applications 7(3), 219–228 (2002)
Fu, K.S.: Learning Control System-Review and Outlook. IEEE Transactions on Automatic Control 15, 210–221 (1970)
An, G., Zhang, L., Liu, J.T.: A Sort of Iterative Learning Algorithm for Tracking Control of Robot Trajectory. Robot. 23(1), 36–39 (2001)
Norrlof, M., Gunnarsson, S.: Experimental Comparison of Some Classical Iterative Learning Control Algorithms. IEEE Transactions on Robotics and Automation 18(4), 636–641 (2002)
Gu, Y.L., Loh, N.K.: Learning Control in Robotic Systems. In: Proceedings of IEEE International Symposium on Intelligent Control, pp. 360–364 (1987)
Togai, M., Yamano, O.: Analysis and Design of an Optimal Learning Control Scheme for Industrial Robots: A Discrete System Approach. In: Proceedings of the 24th IEEE Conferences on Decision and Control, Ft. Lauderdale. Florida, pp. 1399–1404 (1985)
Lee, H.S., Bien, Z.: A Note on Convergence Property of Iterative Learning Controller with Respect to Sup Norm. Automatica 33(8), 525–528 (1997)
Arif, M., Ishihara, T., Inooka, H.: Prediction-based Iterative Learning Control (PILC) for Uncertain Dynamic Nonlinear Systems Using System Identification Technique. Journal of Intelligent and Robotic Systems 27, 291–304 (2000)
Arimoto, S., Kawamura, S., Miyazaki, F.: Bettering Operation of Robots by Learning. Journal of Robotic Systems 1(2), 123–140 (1984)
Moody, J., Darken, C.: Fast Learning in Networks of Locally-Tuned Processing Units. Neural Computation (1), 281–294 (1989)
Cheng, Y.H., Yi, J.Q., Zhao, D.B.: Application of Actor-Critic Learning to Adaptive State Space Construction. In: Proceedings of The Third International Conference on Machine Learning and Cybernetics, pp. 26–29 (2004)
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© 2006 Springer-Verlag Berlin Heidelberg
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Wang, X., Cheng, Y., Sun, W. (2006). Iterative Learning Controller for Trajectory Tracking Tasks Based on Experience Database. In: Yeung, D.S., Liu, ZQ., Wang, XZ., Yan, H. (eds) Advances in Machine Learning and Cybernetics. Lecture Notes in Computer Science(), vol 3930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11739685_81
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DOI: https://doi.org/10.1007/11739685_81
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33584-9
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