吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (02): 469-475.

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Decentralized adaptive iterative learning control for reconfigurable manipulators

LI Yuan-chun1, ZHU Lu2, Dong Bo2, LIU Ke-ping1   

  1. 1. Department of Control Engineering, Changchun University of Technology, Changchun 130012, China;
    2. State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China
  • Received:2010-12-06 Online:2012-03-01 Published:2012-03-01

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Abstract: Based on Lyapunov stability theory and Backstepping technology, a decentralized adaptive iterative learning control algorithm for reconfigurable manipulators was proposed. The dynamics of the reconfigurable manipulators was represented as a set of interconnected subsystems. An adaptive iterative learning control method based on backstepping technology was proposed. Adaptive neural network was introduced to compensate the unknown term and the interconnection term of each subsystem. Simulation examples were presented to demonstrate the effectiveness of the proposed decentralized controller.

Key words: automatic control technology, reconfigurable manipulator, neural network, iterative learning control, Backstepping design

CLC Number: 

  • TP273
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