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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Published in | 2005 American Control Conference pp. 300 - 306 vol. 1 |
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Main Authors | , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
2005
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Subjects | |
Online Access | Get full text |
ISBN | 0780390989 9780780390980 9780780390997 0780390997 |
ISSN | 0743-1619 |
DOI | 10.1109/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. |
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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 |