Adaptive Digital Control Based on Reduced-Order Parameter Identification and Disturbance Observer for Linear Motor

Abstract

This paper proposes a high-efficiency digital servo driver that realizes an auto-tuning feedback and feedforward controller design using on-line parameters identification. Firstly, the variant inertia constant, damping constant and the disturbed load torque of the linear motor are estimated by a reduced-order recursive least square (RLS) estimator, which is composed of a reduced-order RLS estimator and a disturbance torque compensator (DOC). Because one plant parameter is almost constant in high sampling rate digital control for linear motors, the reduced-order RLS estimator will only calculate one plant parameter for efficiency purposes. Furthermore, the auto-tuning algorithm for the feedback and feedforward controller is realized according to the estimated parameters to match the tracking specification. The proposed auto-tuning digital servo controller is evaluated and compared experimentally with a traditional controller on a microcomputer-controlled linear motor positioning system. The experimental results show that this auto-tuning digital servo system remarkably reduces the tracking error of a controlled machine.