Enhancing Positional Accuracy of the XY-Linear Stage Using Laser Tracker Feedback and IT2FLS

Abstract

This paper proposes a calibration algorithm to improve the positional accuracies of an industrial XY-linear stage. Precision positioning of these linear stages is required to maintain highly accurate object handling and manipulation. However, due to imprecisions in linear motor stages and the gearbox, static and dynamic errors exist within these manipulators that cannot be adjusted internally. In this paper, to improve the positioning accuracy of these manipulators, measurements from a laser tracker are used within an interval type-2 fuzzy logic system. The laser tracker used in this experiment is an AT960-MR, which is a highly accurate noncontact coordinate metrology equipment capable of performing highly accurate robotic measurements. To perform calibration, we use an IT2FLS to find a nonlinear correcting relationship to compensate for position errors. The IT2FLS acts on the commands given to the move stage to find the accurate position of the move stage. To train the IT2FLS, we use particle swarm optimization (PSO) for the antecedent part parameters and Moore–Penrose generalized inverse to estimate the consequent part parameters. Data are split into train/test data to test the efficacy of the proposed algorithm. It is shown that by using the proposed IT2FLS-based calibration approach, the standard deviation of the position errors can be decreased from 86.1μm to 55.9μm, which is a 35.1% improvement. Comparison results with a multilayer perceptron neural network reveal that the proposed IT2FLS-based calibration algorithm outperforms multilayer perceptron neural network for positional calibration purposes.