Locally weighted learning model predictive control for nonlinear and time varying dynamics

Lehnert, Christopher & Wyeth, Gordon (2013) Locally weighted learning model predictive control for nonlinear and time varying dynamics. In Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), IEEE, Kongresszentrum Karlsruhe, Karlsruhe, Germany, 2619 -2625.

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Abstract

This paper proposes an online learning control system that uses the strategy of Model Predictive Control (MPC) in a model based locally weighted learning framework. The new approach, named Locally Weighted Learning Model Predictive Control (LWL-MPC), is proposed as a solution to learn to control robotic systems with nonlinear and time varying dynamics. This paper demonstrates the capability of LWL-MPC to perform online learning while controlling the joint trajectories of a low cost, three degree of freedom elastic joint robot. The learning performance is investigated in both an initial learning phase, and when the system dynamics change due to a heavy object added to the tool point. The experiment on the real elastic joint robot is presented and LWL-MPC is shown to successfully learn to control the system with and without the object. The results highlight the capability of the learning control system to accommodate the lack of mechanical consistency and linearity in a low cost robot arm.

Impact and interest:

2 citations in Scopus
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ID Code: 66452
Item Type: Conference Paper
Refereed: Yes
Keywords: Learning (artificial intelligence), Predictive control, Robot dynamics
DOI: 10.1109/ICRA.2013.6630936
ISBN: 9781467356411
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2013 IEEE
Deposited On: 22 Jan 2014 02:00
Last Modified: 30 Jan 2014 03:33

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