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Empirical Inference Conference Paper 2007

Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces

A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means o f a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller.

Author(s): Deisenroth, MP. and Weissel, F. and Ohtsuka, T. and Hanebeck, UD.
Book Title: ECC‘07
Journal: Proceedings of the 9th European Control Conference 2007 (ECC‘07)
Pages: 3664-3671
Year: 2007
Month: July
Day: 0
Bibtex Type: Conference Paper (inproceedings)
Event Name: 9th European Control Conference
Event Place: Kos, Greece
Digital: 0
Electronic Archiving: grant_archive
Institution: EUCA
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik
Links:

BibTex 

@inproceedings{4260,
  title = {Online-Computation Approach to Optimal Control of Noise-Affected Nonlinear Systems with Continuous State and Control Spaces},
  journal = {Proceedings of the 9th European Control Conference 2007 (ECC‘07)},
  booktitle = {ECC‘07},
  abstract = {A novel online-computation approach to optimal control of nonlinear, noise-affected systems with continuous state and control spaces is presented. In the proposed algorithm, system noise is explicitly incorporated into the control decision. This leads to superior results compared to state-of-the-art nonlinear controllers that neglect this influence. The solution of an optimal nonlinear controller for a corresponding deterministic system is employed to find a meaningful state space restriction. This restriction is obtained by means of approximate state prediction using the noisy system equation. Within this constrained state space, an optimal closed-loop solution for a finite decision-making horizon (prediction horizon) is determined within an adaptively restricted optimization space. Interleaving stochastic dynamic programming and value function approximation yields a solution to the considered optimal control problem. The enhanced performance of the proposed discrete-time controller is illustrated by means o
  f a scalar example system. Nonlinear model predictive control is applied to address approximate treatment of infinite-horizon problems by the finite-horizon controller.},
  pages = {3664-3671},
  organization = {Max-Planck-Gesellschaft},
  institution = {EUCA},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2007},
  slug = {4260},
  author = {Deisenroth, MP. and Weissel, F. and Ohtsuka, T. and Hanebeck, UD.},
  month_numeric = {7}
}