2006年
Finite-horizon optimal state-feedback control of nonlinear stochastic systems based on a minimum principle
IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems
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- 開始ページ
- 371
- 終了ページ
- 376
- 記述言語
- 英語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1109/MFI.2006.265616
In this paper, an approach to the finite-horizon optimal state-feedback control problem of nonlinear, stochastic, discrete-time systems is presented. Starting from the dynamic programming equation, the value function will be approximated by means of Taylor series expansion up to second-order derivatives. Moreover, the problem will be reformulated, such that a minimum principle can be applied to the stochastic problem. Employing this minimum principle, the optimal control problem can be rewritten as a two-point boundary-value problem to be solved at each time step of a shrinking horizon. To avoid numerical problems, the two-point boundary-value problem will be solved by means of a continuation method. Thus, the curse of dimensionality of dynamic programming is avoided, and good candidates for the optimal state-feedback controls are obtained. The proposed approach will be evaluated by means of a scalar example system. © 2006 IEEE.
- ID情報
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- DOI : 10.1109/MFI.2006.265616
- SCOPUS ID : 40949137899