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Radial basis function neural network based adaptive fast nonsingular terminal sliding mode controller for piezo positioning stage

  • Intelligent Control and Applications
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Abstract

This paper presents an adaptive fast nonsingular terminal sliding mode control base on a neural network based approximation technique to control the position of a piezo positioning stage (PSS). The proposed terminal sliding mode control can provide faster convergence and higher precision control while maintain its robustness to uncertainties. In the proposed control scheme, the combination of the fast-nonsingular terminal sliding mode control and neural network, which can precisely estimate the uncertainties in dynamic of the PSS system by employing an online tuning scheme, is a promising control approach for actuator systems. In addition, the robust control term is adopted to compensate the modeling error and ensure the robustness corresponding to a bounded disturbance. Stability of the closed loop system is analyzed and proved by using special Lyapunov functions. Experiment results strongly confirm the effectiveness of the proposed control method.

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Authors and Affiliations

  1. School of Mechanical Engineering, University of Ulsan, 93 Daehak-ro, Namgu, Ulsan, Korea

    To Xuan Dinh & Kyoung Kwan Ahn

Authors
  1. To Xuan Dinh
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  2. Kyoung Kwan Ahn
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Corresponding author

Correspondence to Kyoung Kwan Ahn.

Additional information

Recommended by Associate Editor Yang Tang under the direction of Editor Hamid Reza Karimi. This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT and Future Planning (NRF-2017R1A2B3004625).

Kyoung Kwan Ahn received his B.S. degree from the Department of Mechanical Engineering at Seoul National University, Seoul, Korea, in 1990, an M.S. degree in Mechanical Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 1992, and a Ph.D. degree with the thesis entitled “A study on the automation of out-door tasks using a two-link electro-hydraulic manipulator” from the Tokyo Institute of Technology in 1999. He is currently a professor in the School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea. His research interests are the design and control of smart actuators using smart materials, fluid power control, rehabilitation robots, and active damping controls. He is a member of IEEE, ASME, SICE, RSJ, JSME, KSME, KSPE, KSAE, KFPS, and JFPS.

To Xuan Dinh received his B.S. degree from the Department of Mechanical Engineering at Le Quy Don Technical University, Hanoi, Vietnam, in 2012. He is currently a Ph.D. candidate in the School of Mechanical and Automotive Engineering, University of Ulsan, Ulsan, Korea. His research interests include smart actuators/materials, robotics, and nonlinear control.

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Dinh, T.X., Ahn, K.K. Radial basis function neural network based adaptive fast nonsingular terminal sliding mode controller for piezo positioning stage. Int. J. Control Autom. Syst. 15, 2892–2905 (2017). https://doi.org/10.1007/s12555-016-0650-1

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  • DOI: https://doi.org/10.1007/s12555-016-0650-1

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