Novel LPV System Identification for a Gantry Stage: A Global Approach with Adjustable Basis Functions

Abstract

Robotic gantry stages are a prevalent class of industrial robots used for precise positioning tasks in various fields, including semiconductor manufacturing, 3D printing, and automated assembly. However, these systems often exhibit time-varying dynamics because the position of the end-effector (i.e., the payload) shifts the mass/inertia properties. Such dynamic variations are not captured by conventional Linear Time-Invariant (LTI) models, leading to modeling inaccuracies and degraded control performance. Linear Parameter-Varying (LPV) system identification is a more suitable alternative, but existing approaches typically employ a single, fixed basis-function order for all parameters, resulting in excessive model complexity and poor efficiency.This paper presents a novel global LPV system identification method for multi-axis robotic gantry systems, enabling independent basis-function order selection for each parameter. By eliminating unnecessary high-order terms, the method reduces computational overhead and enhances modeling accuracy. Experimental validation on an industrial gantry testbed confirms superior precision and robustness compared to conventional LPV approaches with uniform polynomial orders.