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Decentralized adaptive iterative learning control for interconnected systems with uncertainties

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Abstract

In many applications, the system dynamics allows the decomposition into lower dimensional subsystems with interconnections among them. This decomposition is motivated by the ease and flexibility of the controller design for each subsystem. In this paper, a decentralized model reference adaptive iterative learning control scheme is developed for interconnected systems with model uncertainties. The interconnections in the dynamic equations of each subsystem are considered with unknown boundaries. The proposed controller of each subsystem depends only on local state variables without any information exchange with other subsystems. The adaptive parameters are updated along iteration axis to compensate the interconnections among subsystems. It is shown that by using the proposed decentralized controller, the states of the subsystems can track the desired reference model states iteratively. Simulation results demonstrate that, utilizing the proposed adaptive controller, the tracking error for each subsystem converges along the iteration axis.

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Authors and Affiliations

  1. Department of Control Science and Engineering, Zhejiang University, Hangzhou Zhejiang, 310027, China

    Lili Sun & Tiejun Wu

Authors
  1. Lili Sun
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  2. Tiejun Wu
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Corresponding author

Correspondence to Lili Sun.

Additional information

This work was supported by the National Grand Fundamental Research 973 Program of China (No. G2002CB312200).

Lili SUN received her B.E. degree in Electrical Engineering and the M.S. degree in Control Theory and Control Engineering, both from Shenyang University of Technology, Shenyang, China, in 1998 and 2004, respectively. She is currently working toward Ph.D. degree in Control Science and Engineering at Zhejiang University, Hangzhou, China. Her research interests include mainly related to adaptive iterative learning control, interconnected systems, large-scale systems and robotic systems.

Tiejun WU received his B.E. degree in Chemical Process Control and Ph.D. degree in Control Science and Engineering, both from Zhejiang University, Hangzhou, China, in 1982 and 1988, respectively. In 1989–1991, he was a Postdoctoral Research Fellow with the Systems Research Center, University of Maryland at College Park, USA. Since 1993 he has been with the Department of Control Science and Engineering, Zhejiang University, Hangzhou, China, as a full Professor. He was the vice director of the National Laboratory of Industrial Control Technology, China, from 1993 to 1998, and is currently an Executive Committee Member of Chinese Association for Artificial Intelligence. His research interests include intelligent robot control, computational intelligence and complex systems optimization.

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Sun, L., Wu, T. Decentralized adaptive iterative learning control for interconnected systems with uncertainties. J. Control Theory Appl. 10, 490–496 (2012). https://doi.org/10.1007/s11768-012-0255-z

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  • DOI: https://doi.org/10.1007/s11768-012-0255-z

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