State estimation and feedforward tremor suppression for a handheld micromanipulator with a Kalman filter

Active compensation of physiological tremor for handheld micromanipulators depends on fast control and actuation responses. Because of real-world latencies, real-time compensation is usually not completely effective at eliminating unwanted hand motion. By modeling tremor, more effective cancellation is possible by anticipating future hand motion. We propose a feedforward control strategy that utilizes tremor velocity from a state-estimating Kalman filter. We demonstrate that estimating hand motion in a feedforward controller overcomes real-world latencies in micromanipulator actuation. In hold-still tasks with a fully handheld micromanipulator, the proposed feedforward approach improves tremor rejection by over 50%.