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Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/3670

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Optimal Control With Adaptive Internal Dynamics Models.pdf654.81 kBAdobe PDFView/Open
Title: Optimal control with adaptive internal dynamics models
Authors: Mitrovic, Djordje
Klanke, Stefan
Vijayakumar, Sethu
Issue Date: 2008
Journal Title: Proc. Fifth International Conference on Informatics in Control, Automation and Robotics (ICINCO '08)
Abstract: Optimal feedback control has been proposed as an attractive movement generation strategy in goal reaching tasks for anthropomorphic manipulator systems. The optimal feedback control law for systems with non-linear dynamics and non-quadratic costs can be found by iterative methods, such as the iterative Linear Quadratic Gaussian (iLQG) algorithm. So far this framework relied on an analytic form of the system dynamics, which may often be unknown, difficult to estimate for more realistic control systems or may be subject to frequent systematic changes. In this paper, we present a novel combination of learning a forward dynamics model within the iLQG framework. Utilising such adaptive internal models can compensate for complex dynamic perturbations of the controlled system in an online fashion. The specific adaptive framework introduced lends itself to a computationally more efficient implementation of the iLQG optimisation without sacrificing control accuracy – allowing the method to scale to large DoF systems.
Keywords: Learning dynamics
optimal control
adaptive control
robot simulation
URI: http://hdl.handle.net/1842/3670
Appears in Collections:Informatics Publications

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