Data-driven policy iteration algorithm for optimal control of continuous-time Itô stochastic systems with Markovian jumps

This studies the infinite horizon optimal control problem for a class of continuous-time systems subjected to multiplicative noises and Markovian jumps by using a data-driven policy iteration algorithm. The optimal control problem is equivalent to solve a stochastic coupled algebraic Riccatic equati...

Celý popis

Uloženo v:

Podrobná bibliografie
Vydáno v:	IET control theory & applications Ročník 10; číslo 12; s. 1431 - 1439
Hlavní autoři:	Song, Jun, He, Shuping, Liu, Fei, Niu, Yugang, Ding, Zhengtao
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	The Institution of Engineering and Technology 08.08.2016
Témata:	Algorithms continuous time systems continuous‐time Itô stochastic systems convergence of numerical methods implicit iterative algorithm infinite horizon optimal control problem Iterative algorithms Iterative methods Markov processes Markovian jumps Mathematical analysis Mathematical models multiplicative noises offline iteration algorithm Optimal control parallel Kleinman iterative equations Policies Riccati equations Special Issue: Data-driven Control, and Data-Based System Modelling, Monitoring, and Control stochastic CARE stochastic coupled algebraic Riccatic equation stochastic systems Stochasticity ST‐based data‐driven policy iteration algorithm subsystems transformation technique transforms iterative methods stochastic coupled algebraic Riccatic equation multiplicative noises subsystems transformation technique parallel Kleinman iterative equations transforms optimal control offline iteration algorithm convergence of numerical methods ST-based data-driven policy iteration algorithm Riccati equations infinite horizon optimal control problem continuous-time Itô stochastic systems stochastic systems Markov processes Markovian jumps continuous time systems implicit iterative algorithm stochastic CARE
ISSN:	1751-8644, 1751-8652
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Popis
Shrnutí:	This studies the infinite horizon optimal control problem for a class of continuous-time systems subjected to multiplicative noises and Markovian jumps by using a data-driven policy iteration algorithm. The optimal control problem is equivalent to solve a stochastic coupled algebraic Riccatic equation (CARE). An off-line iteration algorithm is first established to converge the solutions of the stochastic CARE, which is generalised from an implicit iterative algorithm. By applying subsystems transformation (ST) technique, the off-line iterative algorithm is decoupled into N parallel Kleinman's iterative equations. To learn the solution of the stochastic CARE from N decomposed linear subsystems data, an ST-based data-driven policy iteration algorithm is proposed and the convergence is proved. Finally, a numerical example is given to illustrate the effectiveness and applicability of the proposed two iterative algorithms.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1751-8644 1751-8652
DOI:	10.1049/iet-cta.2015.0973