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Gain-scheduled sliding-mode-type iterative learning control design for mechanical systems

Yao, Qijia, Jahanshahi, Hadi, Bekiros, Stelios, Mihalache, Sanda Florentina and Alotaibi, Naif D. (2022) Gain-scheduled sliding-mode-type iterative learning control design for mechanical systems. Mathematics, 10 (16). ISSN 2227-7390

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Identification Number: 10.3390/math10163005

Abstract

In this paper, a novel gain-scheduled sliding-mode-type (SM-type) iterative learning (IL) control approach is proposed for the high-precision trajectory tracking of mechanical systems subject to model uncertainties and disturbances. Based on the SM variable, the proposed controller is synthesized involving a feedback regulation item, a feedforward learning item, and a robust switching item. The feedback regulation item is adopted to regulate the position and velocity tracking errors, the feedforward learning item is applied to handle the model uncertainties and repetitive disturbance, and the robust switching item is introduced to compensate the nonrepetitive disturbance and linearization residual error. Moreover, the gain-scheduled mechanism is employed for both the feedback regulation item and feedforward learning item to enhance the convergence speed. Convergence analysis illustrates that the position and velocity tracking errors can eventually regulate to zero under the proposed controller. By combining the advantages of both SM control and IL control, the proposed controller has strong robustness against model uncertainties and disturbances. Lastly, simulations and comparisons are provided to evaluate the efficiency and excellent performance of the proposed control approach.

Item Type: Article
Official URL: https://www.mdpi.com/journal/mathematics
Additional Information: © 2022 The Author(s).
Divisions: LSE
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > TA Engineering (General). Civil engineering (General)
Date Deposited: 04 Oct 2022 09:54
Last Modified: 17 Oct 2022 13:18
URI: http://eprints.lse.ac.uk/id/eprint/116893

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