In this paper, an effective and simple internal model control (IMC) with extended Kalman filter (EKF) is presented for low-velocity smoothness control of air bearing linear feed stage (ABLFS) driven by permanent magnet synchronous linear motors (PMLSMs). Firstly, the ABLFS is modeled as an inertia system with the nonlinear force ripples which correlates with phase current. The identification experiment is conducted using white noise method to get the approximate linear model of the air-bearing stage. Then, a typical linear feedback controller is derived from the standard IMC principle for the fundamental close-loop control. However, the IMC controller is sensitive to disturbances and modal uncertainties which limits the tracking accuracy. To overcome these drawbacks, the Kalman filter is employed as a state observer which makes the optimal estimation of system state variables (current, velocity and displacement). Since the Kalman filter is a synthesis process by calculating the probability density of measured values and predicted values, the results are more reliable to take place of the encoder feedback. Finally, the effectiveness of this modified method is validated by comparative experiments on a practical ABLFS system.
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