Data-Based Youla Parameterization for Robust Disturbance Observer Design of VCM Motion Stage

Robust disturbance rejection in voice coil motor (VCM) motion stages is often limited by model uncertainties and the difficulty of obtaining accurate plant inverses. To address this issue, this paper develops a data-based Youla parameterization method for designing a robust disturbance observer (DOB) without relying on an analytical plant model. Frequency response data from the VCM stage are measured directly under multiple operating conditions. The Youla parameter Q is expanded using a Laguerre orthogonal basis, and its coefficients are optimized by solving a convex problem that enforces H∞ robust stability and H2 average tracking error constraints on a finite frequency grid. Experiments on a VCM motion stage demonstrate that the optimized Q filter effectively estimates and rejects electromagnetic noise and other disturbances. A total of 30 groups of data covering the full range of operating conditions were used for optimization, and 10 randomly designed experiments were conducted to validate the controller, with the maximum average error below 0.05%. Repetitive tests were carried out to verify the tracking performance for 1 Hz sinusoidal and triangular signals. The results show that the average RMSEs of the proposed method is 0.87% and 0.59%, respectively, which are lower than those of the ITAE-PID, ADRC and K0 controllers. Finally, the robustness of the proposed method is further verified by analyzing the sensitivity function of the closed-loop system.