Sensor Fusion and Control Applied to Industrial Manipulators

Abstract

This thesis by Patrik Axelsson at Linköping University explores advanced sensor fusion and control techniques applied to industrial robots, specifically focusing on enhancing the precision and efficiency of robotic manipulators. The research investigates the use of Bayesian estimation methods, such as the extended Kalman filter and particle filter, to estimate the position of an end-effector. It addresses the challenges posed by flexibilities in robotic joints and links, introduced by cost-optimized design. The work proposes solutions for estimating process noise covariance and discusses various control strategies, including norm-optimal iterative learning control and H∞ control synthesis. The findings are applied in simulations and practical experiments on industrial robots, demonstrating improved dynamic performance. This research is aimed at engineers and researchers in the field of robotics and control systems. Intro -- Abstract -- Populärvetenskaplig sammanfattning -- Acknowledgments -- Contents -- Notation -- 1 Introduction -- 2 Industrial Robots -- 3 Estimation Theory -- 4 Control Theory -- 5 Concluding Remarks -- Bibliography.