Precise tracking control via iterative learning for one-sided Lipschitz Caputo fractional-order systems

Hanjiang Wu; Jie Huang; Kehan Wu; António M. Lopes; Liping Chen; Hanjiang Wu; Jie Huang; Kehan Wu; António M. Lopes; Liping Chen

doi:10.3934/mbe.2024137

Mathematical Biosciences and Engineering

2024, Volume 21, Issue 2: 3095-3109. doi: 10.3934/mbe.2024137

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Precise tracking control via iterative learning for one-sided Lipschitz Caputo fractional-order systems

1.
Anhui Electrical Engineering Professional Technique College, Hefei 230051, China
2.
LAETA/INEGI, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
3.
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China

Academic Editor: Jose M. Rodriguez

Received: 12 December 2023 Revised: 23 January 2024 Accepted: 25 January 2024 Published: 31 January 2024

This paper investigates iterative learning control for Caputo fractional-order systems with one-sided Lipschitz nonlinearity. Both open- and closed-loop P-type learning algorithms are proposed to achieve perfect tracking for the desired trajectory, and their convergence conditions are established. It is shown that the algorithms can make the output tracking error converge to zero along the iteration axis. A simulation example illustrates the application of the theoretical findings, and shows the effectiveness of the proposed approach.
- fractional-order systems,
- one-sided Lipschitz nonlinearity,
- P-type learning algorithm,
- iterative learning control
Citation: Hanjiang Wu, Jie Huang, Kehan Wu, António M. Lopes, Liping Chen. Precise tracking control via iterative learning for one-sided Lipschitz Caputo fractional-order systems[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 3095-3109. doi: 10.3934/mbe.2024137

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Abstract

This paper investigates iterative learning control for Caputo fractional-order systems with one-sided Lipschitz nonlinearity. Both open- and closed-loop P-type learning algorithms are proposed to achieve perfect tracking for the desired trajectory, and their convergence conditions are established. It is shown that the algorithms can make the output tracking error converge to zero along the iteration axis. A simulation example illustrates the application of the theoretical findings, and shows the effectiveness of the proposed approach.

References

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