Locally weighted learning model predictivek control for elastic joint robots
Lehnert, Christopher & Wyeth, Gordon (2012) Locally weighted learning model predictivek control for elastic joint robots. In Carnegie, Dale (Ed.) Proceedings of the 2012 Australasian Conference on Robotics & Automation, Australian Robotics & Automation Association, Victoria University of Wellington, Wellington, New Zealand.
This paper proposes an efficient and online learning control system that uses the successful Model Predictive Control (MPC) method in a model based locally weighted learning framework. The new approach named Locally Weighted Learning Model Predictive Control (LWL-MPC) has been proposed as a solution to learn to control complex and nonlinear Elastic Joint Robots (EJR). Elastic Joint Robots are generally difficult to learn to control due to their elastic properties preventing standard model learning techniques from being used, such as learning computed torque control. This paper demonstrates the capability of LWL-MPC to perform online and incremental learning while controlling the joint positions of a real three Degree of Freedom (DoF) EJR. An experiment on a real EJR is presented and LWL-MPC is shown to successfully learn to control the system to follow two different figure of eight trajectories.
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|Item Type:||Conference Paper|
|Keywords:||Learning control system, Model predictive control, Robotics, Weighted learning framework|
|Subjects:||Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2012 Please consult the authors|
|Deposited On:||05 Mar 2013 23:36|
|Last Modified:||12 Jun 2013 15:34|
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