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.

A Multi-Input-Multi-Output (MIMO) system has complex interactions between inputs and outputs, resulting in a coupling effect that can lead to undesired motions. To effectively control MIMO systems, decoupling is necessary. This paper introduces a decoupling method that uses a transformation matrix modeled as a transfer function using Frequency Response Function (FRF). Unlike the conventional decoupling method uses constant transformation matrix obtained through Canonical Polyadic Decomposition (CPD) and shows performance degradation in certain frequency bands, the proposed method creates transformation matrix as a transfer function and demonstrates better decoupling performance across the entire frequency band. The performance of the proposed method is validated through simulations on a hybrid dual-drive gantry stage.

In this paper, the parametric identification is addressed by a kernel-based model with covariance and a novel model order selection algorithm. The kernel-based model is uti-lized for training the sampled frequency response characteristics, which is insufficient for parametric identification because of noisy and discrete data. The kernel-based frequency response model improves the parametric identification by using the high covariance data. In addition, prior knowledge of the model order is essential for parametric identification. This paper proposes a novel model order selection based on the robust stability criterion of disturbance observer (DOB). The effectiveness of the proposed algorithm is verified through numerical simulations under several conditions.

This article proposes a new data-driven optimization of integrated control for flexible systems to achieve high-performance automatic control. The integrated control that is composed of feedback control, feedforward control, and disturbance observer is adopted in this article as the control framework that can effectively address the control problems of the flexible system. However, it is difficult to optimize all the parameters of the integrated control, because the number of the parameters to be optimized is larger than the conventional feedback control, which complicates the optimization procedure. In this article, the optimization procedure of the integrated control as well as the mathematical background of it is proposed. At first, the closed-loop characteristics of the integrated control are analyzed and its convexity with respect to control parameters is theoretically investigated. The proposed optimization method is designed taking into consideration the convexity of the control configuration to guarantee the global optimality of the obtained parameters. Moreover, the proposed method can simultaneously optimize all the parameters of the integrated controller based on the experimental data. The effectiveness of the proposed algorithm is experimentally confirmed using a flexible system under the following two conditions: first, change of initial parameters and second, change of plant conditions.

Linear motors with large thrust and position accuracy have been used in a variety of industrial robot applications. Despite the advantages of the linear motor, precise control of the linear motor has been a challenging issue due to the cogging force of the linear motor. There have been many studies aimed at rejecting the unwanted cogging force of these linear motors. In this paper, a peak filter and a frequency estimation method are used to reduce the speed ripple of a linear motor. To do this, the peak filter design parameters are investigated and the performance of the frequency estimation technique for the peak filter is verified.