Input and state estimation for compliant motion stages

Abstract

To improve Active Vibration control methods like Independent Modal Space Control (IMSC), an estimate of the modal contribution of vibrational eigenmodes of compliant structures is required. In this work, three Recursive Bayesian input and state estimation algorithms previously introduced in civil engineering are evaluated for use on on high-tech compliant mechanisms to estimate modal contributions for use in Active Vibration Control. The difference in environment when applying these algorithms from civil engineering into high-tech compliant motion stages allows for a significantly different sensor configuration possibly improving estimation performance.

The three algorithms, namely, the Augmented Kalman Filter (AKF), Dual Kalman Filter (DKF) and Gilijns de Moor Filter (GDF) are implemented in simulation and on an experimental compliant motion stage setup. The modal contributions of the guidance flexures are estimated through a noisy acceleration measurement at the stage and strain measurement at the flexure base.

For validation, the filters are evaluated on overall fit quality and system acceleration dependency through the quality of estimation of the flexure tip location. The DKF is shown worst performance with lowest fit scores overall and high acceleration dependency while the AKF and GDF perform comparably well. The GDF is shown to transmit less noise into the estimate however, and thus performed overall best.