Kernel-based auto-associative P-type iterative learning control strategy

doi:10.23919/JSEE.2020.000015

Journal of Systems Engineering and Electronics ›› 2020, Vol. 31 ›› Issue (2): 383-392.doi: 10.23919/JSEE.2020.000015

• Control Theory and Application • Previous Articles Next Articles

Kernel-based auto-associative P-type iterative learning control strategy

Tianyi LAN(), Hui LIN*(), Bingqiang LI()

School of Automation, Northwestern Polytechnical University, Xioan 710129, China

Received:2019-05-27 Online:2020-04-01 Published:2020-04-30
Contact: Hui LIN E-mail:iamlty1111@163.com;linhui@nwpu.edu.cn;libingqiang@nwpu.edu.cn
About author:LAN Tianyi was born in 1981. He received his B.S. and M.S. degrees both in applied mathematics and Ph.D. degree in control science and engineering from Northwestern Polytechnical University in 2004, 2007 and 2017, respectively. He is currently a postdoctoral fellow with the School of Automation, Northwestern Polytechnical University. His research interests include iterative learning control, fractional order control, control theory, and multi-agent system. E-mail: iamlty1111@163.com|LIN Hui was born in 1957. He received his B.S. and M.S. degrees in electrical engineering and his Ph.D. degree in control science engineering from Northwestern Polytechnical University in 1982, 1985, and 1993, respectively. He is a currently a professor at the Department of Electrical Engineering, Northwestern Polytechnical University. His research interests include power electronics, electric vehicles, motion control, and control theory. E-mail: linhui@nwpu.edu.cn|LI Bingqiang was born in 1982. He received his B.E. and M.E. degrees both in electrical engineering and Ph.D. degree in control science and engineering from Northwestern Polytechnical University in 2004, 2007 and 2010, respectively. He is currently an associate professor and the dean of the Department of Electrical Engineering, Northwestern Polytechnical University, a postdoctoral fellow with Xi'an Aviation Brake Technology Co., Ltd.. His research interests include iterative learning control, fractional order control, servo control, multi-agent system and their applications. E-mail: libingqiang@nwpu.edu.cn
Supported by:
the National Natural Science Foundation of China(51777170);the Aeronautical Science Foundation of China(20162853026);the Project Supported by Natural Science Basic Research Plan in Shannxi Province of China(2019JM-462);the Project Supported by Natural Science Basic Research Plan in Shannxi Province of China(2020JM-151);This work was supported by the National Natural Science Foundation of China (51777170), the Aeronautical Science Foundation of China (20162853026), and the Project Supported by Natural Science Basic Research Plan in Shannxi Province of China (2019JM-462; 2020JM-151)

Abstract

Abstract:

In order to accelerate the convergence speed of iterative learning control (ILC), taking the P-type learning algorithm as an example, a correction algorithm with kernel-based auto-associative is proposed for the linear system. The learning mechanism of human brain associative memory is introduced to the traditional ILC. The control value of the subsequent time is pre-corrected with the current time information by association in each iterative learning process. The learning efficiency of the whole system is improved significantly with the proposed algorithm. Through the rigorous analysis, it shows that under this new designed ILC scheme, the uniform convergence of the state tracking error is guaranteed. Numerical simulations illustrate the effectiveness of the proposed associative control scheme and the validity of the conclusion.

Key words: iterative learning control (ILC), associative learning, convergence speed, tracking convergence

Tianyi LAN, Hui LIN, Bingqiang LI. Kernel-based auto-associative P-type iterative learning control strategy[J]. Journal of Systems Engineering and Electronics, 2020, 31(2): 383-392.

Figures/Tables 7

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References 35

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Error	1	2	$\cdots$	$i$	$i+1$	$\cdots$	$N$
${{{\mathit{\boldsymbol{e}}}}}_k (0)$	$d(0, 1){{{\mathit{\boldsymbol{e}}}}}_k (0)$	$d(0, 2){{{\mathit{\boldsymbol{e}}}}}_k (0)$	$\cdots$	$d(0, i){{{\mathit{\boldsymbol{e}}}}}_k (0)$	$d(0, i + 1){{{\mathit{\boldsymbol{e}}}}}_k (0)$	$\cdots$	$d(0, N){{{\mathit{\boldsymbol{e}}}}}_k (0)$
${{{\mathit{\boldsymbol{e}}}}}_k (1)$	0	$d(1, 2){{{\mathit{\boldsymbol{e}}}}}_k (1)$	$\cdots$	$d(1, i){{{\mathit{\boldsymbol{e}}}}}_k (1)$	$d(1, i +1){{{\mathit{\boldsymbol{e}}}}}_k (1)$	$\cdots$	$d(1, N){{{\mathit{\boldsymbol{e}}}}}_k (1)$
${{{\mathit{\boldsymbol{e}}}}}_k (2)$	0	0	$\cdots$	$d(2, i){{{\mathit{\boldsymbol{e}}}}}_k (2)$	$d(2, i + 1){{{\mathit{\boldsymbol{e}}}}}_k (2)$	$\cdots$	$d(2, N){{{\mathit{\boldsymbol{e}}}}}_k (2)$
$ \vdots $	$ \vdots $	$ \vdots $	$ \ddots $	$ \vdots $	$\vdots $	$ \ddots $	$ \vdots $
${{{\mathit{\boldsymbol{e}}}}}_k (i)$	0	0	$\cdots$	0	$d(i, i + 1){{{\mathit{\boldsymbol{e}}}}}_k (i)$	$\cdots$	$d(i, N){{{\mathit{\boldsymbol{e}}}}}_k (i)$
$ \vdots $	$ \vdots $	$ \vdots $	$\cdots$	$ \vdots $	$\vdots $	$\cdots$	$ \vdots $
${{{\mathit{\boldsymbol{e}}}}}_k (N - 1)$	0	0	$\cdots$	0	0	0	$d(N - 1, N){{{\mathit{\boldsymbol{e}}}}}_k (N - 1)$

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Kernel-based auto-associative P-type iterative learning control strategy

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