Estimation of Multi-state Dependent Disturbance by Using Multi-dimensional Gaussian Process

Abstract

High-precision linear motor stages have been widely used for their excellent positioning accuracy and speed. However, core-type linear motor stages have performance limitations because of various nonlinear factors including cogging force, friction, and geometrical imbalance. This paper analyzes disturbances in velocity and position domains and trains a Two-Input-Single-Output (TISO) nonlinear model using the Gaussian process for the disturbance. With this, two state-dependent disturbances are removed effectively. As a result, the control performance with a proposed controller is enhanced. Ultimately, this paper introduces three contribution points: 1) analysis of disturbances based on position/velocity, 2) design of TISO Gaussian process model, and 3) validation of estimation performance of proposed algorithm through simulation. © 2024 IEEE.