Abstract
Hysteresis and vibration are main factors in reducing the accuracy and the speed of a nanopositioner. In order to improve the positioning accuracy and the speed, a robust adaptive resonant damping control is proposed for trajectory tracking of a nanopositioner system driven by a piezoelectric actuator. Radial basis function neural network is employed to approximate unknown nonlinearities in the controller so as to reduce the dependence on model information. Linear and hysteresis model are establish based on nanopositioning stage measured data. The proposed control strategy is verified by simulation using an identified model.
This work is supported by National Natural Science Foundation (NNSF) of China under Grant 61403006, 61873005; Key program of Beijing Municipal Education Commission KZ201810011012; and Support Project of High-level Teachers in Beijing Municipal Universities in the Period of 13th Five-year Plan CIT&TCD201704044.
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Wei, W., Xia, P., Liu, Z., Zuo, M. (2019). Robust Adaptive Resonant Damping Control of Nanopositioning. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11555. Springer, Cham. https://doi.org/10.1007/978-3-030-22808-8_14
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