Optimal control with adaptive internal dynamics models

D. Mitrovic, S. Klanke, S. Vijayakumar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationFifth International Conference on Informatics in Control, Automation and Robotics (ICINCO '08), Madeira, Portugal
Number of pages8
Publication statusPublished - 2008

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