A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems

We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an af...

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Bibliographic Details
Published in2005 American Control Conference pp. 300 - 306 vol. 1
Main Authors Todorov, E., Weiwei Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 2005
Subjects
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ISBN0780390989
9780780390980
9780780390997
0780390997
ISSN0743-1619
DOI10.1109/ACC.2005.1469949

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Summary:We present an iterative linear-quadratic-Gaussian method for locally-optimal feedback control of nonlinear stochastic systems subject to control constraints. Previously, similar methods have been restricted to deterministic unconstrained problems with quadratic costs. The new method constructs an affine feedback control law, obtained by minimizing a novel quadratic approximation to the optimal cost-to-go function. Global convergence is guaranteed through a Levenberg-Marquardt method; convergence in the vicinity of a local minimum is quadratic. Performance is illustrated on a limited-torque inverted pendulum problem, as well as a complex biomechanical control problem involving a stochastic model of the human arm, with 10 state dimensions and 6 muscle actuators. A Matlab implementation of the new algorithm is availabe at www.cogsci.ucsd.edu//spl sim/todorov.
Bibliography:SourceType-Conference Papers & Proceedings-1
ObjectType-Conference Paper-1
content type line 25
ISBN:0780390989
9780780390980
9780780390997
0780390997
ISSN:0743-1619
DOI:10.1109/ACC.2005.1469949