Estimation-based ILC using Particle Filter with Application to Industrial Manipulators
2013 (English)In: Proceedings of the 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013, 1740-1745 p.Conference paper (Refereed)
An estimation-based iterative learning control (ILC) algorithm is applied to a realistic industrial manipulator model. By measuring the acceleration of the end-effector, the arm angular position accuracy is improved when the measurements are fused with motor angle observations. The estimation problem is formulated in a Bayesian estimation framework where three solutions are proposed: one using the extended Kalman filter (EKF), one using the unscented Kalman filter (UKF), and one using the particle filter (PF). The estimates are used in an ILC method to improve the accuracy for following a given reference trajectory. Since the ILC algorithm is repetitive no computational restrictions on the methods apply explicitly. In an extensive Monte Carlo simulation study it is shown that the PF method outperforms the other methods and that the ILC control law is substantially improved using the PF estimate.
Place, publisher, year, edition, pages
2013. 1740-1745 p.
IdentifiersURN: urn:nbn:se:liu:diva-100880DOI: 10.1109/IROS.2013.6696584ISI: 000331367401125OAI: oai:DiVA.org:liu-100880DiVA: diva2:686628
2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Tokyo, Japan, November 3-7, 2013
ProjectsVinnova Excellence Center LINK-SIC