Magnetic levitation (maglev) devices have been extensively studied in the literature and find applications in many engineering fields. In this manuscript, we investigate and discuss the control and state estimation for a nonlinear maglev device based on Kalman estimation theory considering unmodeled process and measurement noise in the formulation. The Continuous Discrete Extended Kalman Filter (CDEKF) is utilized to estimate the system states and subsequently generate the control output based on the estimates. The approach is demonstrated in simulation using actual hardware (maglev and sensor) dynamics and parameter values. Currently, the proposed approach is being implemented and tuned on hardware in the loop (HIL) maglev device. The performance of the proposed approach in the simulated environment for state estimation and system control for step and sinusoidal reference trajectories and the HIL implementation procedure are also discussed.

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