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Generalized Minimum Variance Iterative Learning Speed Control of Ultrasonic Motor

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Abstract

In order to reduce the influence of time-varying disturbance on motion control performance of ultrasonic motor, the speed control strategy of ultrasonic motor is studied in this paper. An iterative learning control strategy including prediction and closed-loop control is proposed by combining iterative learning control with generalized minimum variance self-tuning control. By introducing the previous control information into the objective function and using the design method of the generalized minimum variance control strategy, the generalized minimum variance iterative learning control law is obtained, which has both self-learning and self-adaptive ability. The proposed control strategy is applied to the speed control of ultrasonic motor and validated by simulation and experiment. The results of experiments under different load conditions and different given values show that good control performance can be obtained by adopting the proposed control strategy. The results of intermittent loading experiments indicate that, the ability to adapt to the non-repetitive disturbances such as sudden load mutation is enhanced.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (U1304501).

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Authors and Affiliations

  1. Department of Electrical Engineering, Henan University of Science and Technology, LuoYang, 471023, China

    Shi Jingzhuo & Huang Wenwen

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  1. Shi Jingzhuo
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  2. Huang Wenwen
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Correspondence to Shi Jingzhuo.

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Jingzhuo, S., Wenwen, H. Generalized Minimum Variance Iterative Learning Speed Control of Ultrasonic Motor. J. Electr. Eng. Technol. 16, 2757–2765 (2021). https://doi.org/10.1007/s42835-021-00781-x

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  • DOI: https://doi.org/10.1007/s42835-021-00781-x

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