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Inversion-free force tracking control of piezoelectric actuators using fast finite-time integral terminal sliding-mode

DOI:10.1016/j.mechatronics.2018.11.005 期刊:Mechatronics 出版年份:2019 更新时间:2025-09-23 15:23:52
摘要: The major hurdles to control the force created by piezoelectric actuators (PEAs) are originated from its strong nonlinear behaviors which include hysteresis, creep, and vibration dynamics. To achieve an accurate, fast and robust force tracking performance without using complicated modeling and parameter identification of PEAs, this paper presents a practical direct force control scheme. The proposed controller is based on two core approaches: 1) fast finite-time integral terminal sliding mode (FFI-TSM) which allows fast convergence and high accuracy to the closed-loop system without control chattering; and 2) an inverse-model-free compensation, named force-based time-delayed estimation (FBTDE) which offers significant robustness with minimum use of plant dynamics information. The finite-time stability of the overall closed-loop system is proven through the Lyapunov's method. The proposed force tracking controller is implemented on the PEA system driving a variable physical damping actuator mechanism. The overall accuracy, convergence speed, and robustness of the proposed controller are validated under various experimental scenarios. Comparative experimental results are particularly presented to verify the effectiveness of the FFI-TSM term and the FBTDE term.
作者: Jinoh Lee,Maolin Jin,Navvab Kashiri,Darwin G. Caldwell,Nikolaos G. Tsagarakis
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To achieve an accurate, fast, and robust force tracking performance for piezoelectric actuators without using complicated modeling and parameter identification, by developing an inverse-model-free direct force control scheme.

The proposed inverse-model-free force controller, combining FBTDE and FFI-TSM, provides accurate, fast, and robust force tracking for piezoelectric actuators. It effectively compensates for nonlinearities like hysteresis and creep without complex modeling, and experimental results validate its superiority over previous methods in terms of accuracy, convergence speed, and robustness. Future work could integrate feedforward compensation for enhanced performance.

The control performance is affected by sensor limitations such as bandwidth, resolution, and noise, particularly in micro/nano-scale applications. The method relies on force feedback and may require additional feedforward terms or self-sensing techniques for further improvement in precision.

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