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.
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.
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|Item Type:||Conference Paper|
|Keywords:||Learning (artificial intelligence), Predictive control, Robot dynamics|
|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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