Volume 39, Number 2, April 2021
|Page(s)||400 - 406|
|Published online||09 June 2021|
PDα-type iterative learning control with initial state learning for fractional-order systems
School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710072, China
2 College of Mathematics and Computer Science, Yan'an University, Yan'an 716000, China
3 School of Mathematics and Physical Sciences, Xuzhou Institute of Technology, Xuzhou 221111, China
In order to eliminate the influence of the arbitrary initial state on the systems, open-loop and open-close-loop PDα-type fractional-order iterative learning control (FOILC) algorithms with initial state learning are proposed for a class of fractional-order linear continuous-time systems with an arbitrary initial state. In the sense of Lebesgue-p norm, the sufficient conditions for the convergence of PDα-type algorithms are disturbed in the iteration domain by taking advantage of the generalized Young inequality of convolution integral. The results demonstrate that under these novel algorithms, the convergences of the tracking error are can be guaranteed. Numerical simulations support the effectiveness and correctness of the proposed algorithms.
Key words: fractional-order / initial state learning / iterative learning control / Lebesgue-p norm
关键字 : 分数阶 / 初始状态学习 / 迭代学习控制 / Lebesgue-p范数
© 2021 Journal of Northwestern Polytechnical University. All rights reserved.
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