ADAPTIVE ITERATIVE LEARNING CONTROL OF ROBOTIC SYSTEMS USING BACKSTEPPING DESIGN
2013 ◽
Vol 37
(3)
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pp. 591-601
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Keyword(s):
In this paper, a backstepping adaptive iterative learning control (AILC) is proposed for robotic systems with repetitive tasks. The AILC is designed to approximate unknown certainty equivalent controller. Finally, we apply a Lyapunov like analysis to show that all adjustable parameters and the internal signals remain bounded for all iterations.
2013 ◽
Vol 284-287
◽
pp. 1759-1763
2004 ◽
Vol 10
(5)
◽
pp. 395-401
2000 ◽
Vol 147
(2)
◽
pp. 217-223
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2013 ◽
Vol 479-480
◽
pp. 737-741
2018 ◽
Vol 29
(1)
◽
pp. 232-237
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