Abstract
Piezoelectric actuators are widely employed in the field of micro-/nanomanipulation. However, hysteresis is the dominant issue in piezoelectric actuators, which leads to a great challenge to achieve high precision micromanipulation. Proportional-integral-derivative (PID) control is an efficient approach to reduce hysteresis effect in piezoelectric actuators. However, its parameter tuning is a time-consuming work for PID motion tracking control implementation. In this work, the neural networks (NN) is adopted to provide a functional model for PID with optimized parameters. It enables an intelligent and adaptive motion tracking process. The effectiveness of the presented NN-based PID control scheme is verified by performing simulation studies.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grant 51575545, the Macao Science and Technology Development Fund under Grant 179/2017/A3, and Research Committee of the University of Macau under Grant MYRG2018-00034-FST.
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Yan, Y., Xu, Q. (2019). Neural Networks-Based PID Precision Motion Control of a Piezo-Actuated Microinjector. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11745. Springer, Cham. https://doi.org/10.1007/978-3-030-27529-7_35
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DOI: https://doi.org/10.1007/978-3-030-27529-7_35
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