Abstract
A novel approach to the learning control of non-collocated robotic systems is presented, and its feasibility and effectiveness are verified through experiments using an ultra-high speed direct-drive robot having structural flexibility. This approach, called “progressive learning,” allows the system to learn parameters recursively and progressively, starting with the ones associated with low frequencies and moving up to the ones with a full spectrum. Even though the system has non-collocated sensors and actuators and the relative order is three or higher, the learning process is guaranteed to converge by exciting the system with a particular series of reference inputs having an appropriate frequency spectrum. A controller augmentation technique to accelerate the learning process is also presented.
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References
B.-H. Yang and H. Asada, “Progressive Learning — Part I: Stability Analysis,” Proc. of the 1995 ASME International Mechanical Engineering Congress and Exposition, San Francisco, November, 1995
K. S. Narendra and A. M. Annaswamy, Stable Adaptive Systems, Prince Hall, 1989
Shih-Hung Li, Noriyuki Fujiwara, and Haruhiko Asada, “An Ultrahigh Speed Assembly Robot System: Part I. Design,” Proc. IEEE 1994 Japan-USA Symposium on Flexible Automation, Japan, 1994
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© 1997 Springer-Verlag London Limited
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Li, SH., Yang, BH., Asada, H. (1997). Experimental verification of progressive learning control for high-speed direct-drive robots with structure flexibility and non-collocated sensors. In: Khatib, O., Salisbury, J.K. (eds) Experimental Robotics IV. Lecture Notes in Control and Information Sciences, vol 223. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0035222
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DOI: https://doi.org/10.1007/BFb0035222
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-540-40942-7
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