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A Neural-Repetitive Control Approach for High-Performance Motion Control of Piezo-Actuated Systems

  • Research Article - Mechanical Engineering
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Abstract

This paper presents a neural-repetitive approach to the precision control of piezo-actuated systems. Two neural controllers are used in the proposed control scheme. The first controller is a standard neural adaptive controller using a radial basis function network as a baseline for motion control. To eliminate non-zero periodic errors originating in the deterministic reference signals, an additional neural controller containing a discrete-time repetitive controller was added by introducing solutions of a transformed feedforward control problem constrained by a deterministic internal model. The proposed neural-repetitive controllers were applied to a piezo-actuated system to track periodic and complex motion profiles. The experimental results demonstrate that the proposed neural-repetitive controller improves control performance, showing good robustness pertaining to variations in plant parameters.

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Authors and Affiliations

  1. Department of Mechanical Engineering, National Taiwan University of Science and Technology, 43 Keelung Road, Section 4, Taipei, 10672, Taiwan

    Chi-Ying Lin & Chien-Yao Li

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  1. Chi-Ying Lin
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  2. Chien-Yao Li
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Correspondence to Chi-Ying Lin.

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Lin, CY., Li, CY. A Neural-Repetitive Control Approach for High-Performance Motion Control of Piezo-Actuated Systems. Arab J Sci Eng 39, 4131–4140 (2014). https://doi.org/10.1007/s13369-014-1008-8

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  • DOI: https://doi.org/10.1007/s13369-014-1008-8

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