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<p>The piezoelectric micropositioning stage is a system driven by piezoelectric actuators, whose inherent hysteresis characteristics can signicantly affect the tracking accuracy. Moreover, the system is sensitive to disturbances such as sensor failure in the output channel and controller saturation in the input channel, which also poses a challenge to the design of the controller. To address this issue, this paper developed an adaptive neural controller that has the following significant advantages: <i>(i)</i> The additive and multiplicative faults in the sensor channel are handled during the adaptive neural controller design process, which adopts the method of fault reconstruction and does not require any other dynamic information. <i>(ii)</i> The proposed controller integrates hysteresis compensation within an extended state observer and employs a backstepping-based controller with an intermediate variable, eliminating the need for an accurate hysteresis model or its inverse. <i>(iii)</i> The saturation error of the controller input is also taken into account in observer design, which not only reduces the impact of nonlinearity on the system but also simplifies the overall control structure. The stability of the closed-loop system is rigorously established using Lyapunov theory, and experimental validation on a piezoelectric micropositioning stage confirms the effectiveness of the proposed method.</p>

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Adaptive neural control for piezoelectric micropositioning stage under sensor failure and input saturation

  • 期刊论文

  • 发表日期：

    2026年3月3日

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  • Heyu Hu,
  • Xinyuan Tian,
  • Lijie You,
  • Shengjun Wen

摘要

The piezoelectric micropositioning stage is a system driven by piezoelectric actuators, whose inherent hysteresis characteristics can signicantly affect the tracking accuracy. Moreover, the system is sensitive to disturbances such as sensor failure in the output channel and controller saturation in the input channel, which also poses a challenge to the design of the controller. To address this issue, this paper developed an adaptive neural controller that has the following significant advantages: (i) The additive and multiplicative faults in the sensor channel are handled during the adaptive neural controller design process, which adopts the method of fault reconstruction and does not require any other dynamic information. (ii) The proposed controller integrates hysteresis compensation within an extended state observer and employs a backstepping-based controller with an intermediate variable, eliminating the need for an accurate hysteresis model or its inverse. (iii) The saturation error of the controller input is also taken into account in observer design, which not only reduces the impact of nonlinearity on the system but also simplifies the overall control structure. The stability of the closed-loop system is rigorously established using Lyapunov theory, and experimental validation on a piezoelectric micropositioning stage confirms the effectiveness of the proposed method.