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<p>This paper proposes a long short-term memory (LSTM) prediction-enhanced command-filtered backstepping control (CFBC) method to address the low frequency vibration suppression in high-precision air-bearing active vibration isolation systems. A complete nonlinear dynamic model of the vibration isolation system is firstly established which is transformed into multi-input-multi-output (MIMO) high order form. Next, a LSTM state prediction mechanism based on a Gaussian-weighted sliding window is designed, which predicts the weighted trend of future states from historical sequences to improve prediction smoothness and robustness. Building on this, the LSTM prediction output is integrated as a compensation term with CFBC to construct a composite control architecture with leading and filtering capabilities. Through Lyapunov stability analysis, the boundedness of all closed-loop signals and the convergence of tracking errors are proven. Finally, experimental validation on a real air bearing vibration isolation platform shows that, compared to the traditional CFBC method, the proposed strategy significantly reduces tracking errors and effectively improves vibration suppression performance in the key low-frequency band of 1–5 Hz.</p>

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Lstm-prediction enhanced command-filtered backstepping control for air bearing active vibration isolation nonlinear system

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Sanxia Wang,
  • Junjie Wang,
  • Shuai Liu,
  • Bin Liu

摘要

This paper proposes a long short-term memory (LSTM) prediction-enhanced command-filtered backstepping control (CFBC) method to address the low frequency vibration suppression in high-precision air-bearing active vibration isolation systems. A complete nonlinear dynamic model of the vibration isolation system is firstly established which is transformed into multi-input-multi-output (MIMO) high order form. Next, a LSTM state prediction mechanism based on a Gaussian-weighted sliding window is designed, which predicts the weighted trend of future states from historical sequences to improve prediction smoothness and robustness. Building on this, the LSTM prediction output is integrated as a compensation term with CFBC to construct a composite control architecture with leading and filtering capabilities. Through Lyapunov stability analysis, the boundedness of all closed-loop signals and the convergence of tracking errors are proven. Finally, experimental validation on a real air bearing vibration isolation platform shows that, compared to the traditional CFBC method, the proposed strategy significantly reduces tracking errors and effectively improves vibration suppression performance in the key low-frequency band of 1–5 Hz.