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This paper presents an adaptive sliding mode control (ANSMC) approach for tracking control of piezoelectric actuators (PEA). The proposed method utilizes a radial basis function neural network (RBFNN) to estimate the unknown PEA’s model under the influence of measurement noise. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory, guaranteeing finite-time convergence. Simulation results prove that the proposed approach achieves high-precision motion and robustness against uncertainties and disturbances.

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Adaptive Neural Sliding Mode Control for Motion Tracking of Piezoelectric Actuator

  • 会议论文

  • 发表日期：

    2025年

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  • Nguyen Thi Thu Hang,
  • Nguyen Ngoc Son

摘要

This paper presents an adaptive sliding mode control (ANSMC) approach for tracking control of piezoelectric actuators (PEA). The proposed method utilizes a radial basis function neural network (RBFNN) to estimate the unknown PEA’s model under the influence of measurement noise. The stability of the closed-loop system is rigorously analyzed using Lyapunov theory, guaranteeing finite-time convergence. Simulation results prove that the proposed approach achieves high-precision motion and robustness against uncertainties and disturbances.