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International Conference on Intelligent Computing for Sustainable Energy and Environment

International Conference on Life System Modeling and Simulation

ICSEE 2017, LSMS 2017: Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration pp 740–750Cite as

A Composite Controller for Piezoelectric Actuators with Model Predictive Control and Hysteresis Compensation

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 763))

Abstract

Piezoelectric actuators (PEAs) are ubiquitous in nanopositioning applications due to their high precision, rapid response and large mechanical force. However, precise control of PEAs is a challenging task because of the existence of hysteresis, an inherent strong nonlinear property. To minimize its influence, various control methods have been proposed in the literature, which can be roughly classified into three categories: feedforward control, feedback control and feedforward-feedback control. Feedforward-feedback control combines the advantages of feedforward control and feedback control and turns into a better control scheme. Inspired by this strategy, a composite controller is proposed for the tracking control of PEAs in this paper. Specifically, the model of PEAs is constructed by a multilayer feedforward neural network (MFNN). This model is then instantaneously linearized, which leads to an explicit model predictive control law. Then, an inverse Duhem hysteresis model is adopted as a feedforward compensator to mitigate the hysteresis nonlinearity. Experiments are designed to validate the effectiveness of the proposed method on a piezoelectric nanopositioning stage (P-753.1CD, Physik Instrumente). Comparative experiments are also conducted between the proposed method and some existing control methods. Experimental results demonstrate that the root mean square tracking error of the proposed method is reduced to 16% of that under the previously proposed model predictive controller [16].

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (Grants 61422310, 61633016, 61370032) and Beijing Natural Science Foundation (Grant 41620667).

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Authors and Affiliations

  1. State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China

    Ang Wang & Long Cheng

Authors
  1. Ang Wang
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  2. Long Cheng
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Corresponding author

Correspondence to Long Cheng .

Editor information

Editors and Affiliations

  1. Queen’s University Belfast, Belfast, United Kingdom

    Kang Li

  2. Nanjing Automation Research Institute, Nanjing, China

    Yusheng Xue

  3. Harbin Institute of Technology, Harbin, China

    Shumei Cui

  4. Shanghai University, Shanghai, China

    Qun Niu

  5. Queen’s University Belfast, Belfast, United Kingdom

    Zhile Yang

  6. Cranfield University, Bedford, United Kingdom

    Patrick Luk

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© 2017 Springer Nature Singapore Pte Ltd.

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Wang, A., Cheng, L. (2017). A Composite Controller for Piezoelectric Actuators with Model Predictive Control and Hysteresis Compensation. In: Li, K., Xue, Y., Cui, S., Niu, Q., Yang, Z., Luk, P. (eds) Advanced Computational Methods in Energy, Power, Electric Vehicles, and Their Integration. ICSEE LSMS 2017 2017. Communications in Computer and Information Science, vol 763. Springer, Singapore. https://doi.org/10.1007/978-981-10-6364-0_73

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  • DOI: https://doi.org/10.1007/978-981-10-6364-0_73

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6363-3

  • Online ISBN: 978-981-10-6364-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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