Data-Based Active Vibration Control Strategy for Ultralow Frequency Isolation System Considering Sensor Noise Measurements

The measurement accuracy of ultra-precision instruments such as large-scale scanning electron microscopes (SEMs) is significantly affected by ground micro-vibrations (typically at the VC-D or VC-E level). The ultralow micro-vibration levels and low signal-to-noise ratios impede the accuracy of practical models, posing significant challenges for control system implementation. This article presents a data-based hybrid control strategy for ultralow-frequency vibration isolation to address the sensor noise and model dependency issues that affect the precision of control signals. To reduce the noise proportion in the measurement reference signals, a system state observer is developed to recursively update state estimates using sensor measurement information. A closed-loop feedback (FB) controller based on model predictive control is designed that incorporates the system’s relative displacement, balancing resonance suppression and stability. To further suppress in-band vibrations, a data-driven feedforward (FF) control strategy was constructed, which incorporates a concise and stable generalized FIR filter for online parameter identification and leverages the rapid convergence of the Gauss-Newton iterative algorithm, thereby effectively overcoming the reliance on accurate prior parameters. Simulation and experimental results demonstrate that the proposed hybrid control strategy effectively suppresses vibrations at the system’s natural frequency, constrains relative displacement within 5 $\mu $ m, and significantly reduces the vibration transmissibility in the 0.8–20 Hz frequency range. This research shows tremendous potential for engineering applications in micro-vibration isolation for ultra-precision microscope measurement equipment.