Data-driven fractional order feedback and model-less feedforward control of a XY reluctance-actuated micropositioning stage

Zhang, Xu, Lai, Leijie, Li, Pengzhi ORCID: 0000-0001-8883-1885 and Zhu, Li-Min (2022) Data-driven fractional order feedback and model-less feedforward control of a XY reluctance-actuated micropositioning stage. Review of Scientific Instruments, 93. Art 115002. doi:10.1063/5.0098759

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11903 Zhang, Lai, Li and Zhu (2022) Data-Drive_Fractional_Order_Feedback_and_Model-less_Feedforward_Control_of_a_XY_Reluctance-Actuated_Micropositioning_Stage.pdf - Accepted Version
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This paper proposes a compound data-driven control method to solve the problems of low damping resonance, different dynamic properties, and hysteresis in the large-range compliant micropositioning stage driven by a Maxwell reluctance actuator. First, in order to verify the proposed control algorithm, a reluctance-actuated, XY compliant micropositioning stage is constructed according to the principle of operation of a reluctance actuator. Second, in order to eliminate the influence of complex dynamics on the controller design, a fractional order proportional-integral feedback controller is designed using a data iterative feedback turning algorithm. Third, the finite impulse response feedforward filter is optimized using experimental data, and the on-line inverse estimation of the system frequency response function and its iterative feedforward compensation are carried out to further eliminate the influence of light damping resonance. Finally, the proposed control method is used for tracking the experiment and compared with other methods. The experimental results show that the proposed control method can better meet the requirements of high precision, fast speed, and strong anti-interference ability for large stroke micro/nanopositioning and tracking.

Item Type: Article
Article Type: Article
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
T Technology > T Technology (General)
Divisions: Schools and Research Institutes > School of Business, Computing and Social Sciences
Research Priority Areas: Applied Business & Technology
Depositing User: Kate Greenaway
Date Deposited: 20 Dec 2022 11:13
Last Modified: 13 Mar 2024 13:13

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