State Estimation for Polysolenoid Linear Motor based on an Adaptive Unscented Kalman Filter with Unknown Load and Measurement Noises

The linear motor has been widely applied in industry to provide directly straight motion. The Polysolenoid Linear Motor (PLM), as its name, is one type of the synchronous linear machine. Toward the control problem of the PLM, state observation plays a crucial role due to the lack of measurement. In this paper, an Adaptive Unscented Kalman Filter (AUKF), which can provide reliable information of system state including applied current as well as position and velocity, is proposed. Furthermore, our observer can deal with the uncertainty load force and unknown unbias measurement noises adaptively, which contributes to robust and effective control of PLM with uncertain load condition. A scenario will be made to test the robutness of the algorithm under the value variation of measurement noise covariance. The performance of the system is verified by simulation in an illustrative example.