Stacked adaptive dynamic programming with unknown system model

Pavel Osinenko, Thomas Göhrt, Grigory Devadze, Stefan Streif

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Adaptive dynamic programming is a collective term for a variety of approaches to infinite-horizon optimal control. Common to all approaches is approximation of the infinite-horizon cost function based on dynamic programming philosophy. Typically, they also require knowledge of a dynamical model of the system. In the current work, application of adaptive dynamic programming to a system whose dynamical model is unknown to the controller is addressed. In order to realize the control algorithm, a model of the system dynamics is estimated with a Kalman filter. A stacked control scheme to boost the controller performance is suggested. The functioning of the new approach was verified in simulation and compared to the baseline represented by gradient descent on the running cost.

Original languageEnglish
Pages (from-to)4150-4155
Number of pages6
JournalIFAC-PapersOnLine
Volume50
Issue number1
DOIs
Publication statusPublished - Jul 2017
Externally publishedYes

Keywords

  • Dynamic programming
  • Kalman filters
  • Optimal control

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