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[formula omitted] Tracking learning control for discrete-time Markov jump systems: A parallel off-policy reinforcement learning.
- Source :
-
Journal of the Franklin Institute . Dec2023, Vol. 360 Issue 18, p14878-14890. 13p. - Publication Year :
- 2023
-
Abstract
- This paper deals with the H ∞ tracking control problem for a class of linear discrete-time Markov jump systems, in which the knowledge of system dynamics is not required. First, combined with reinforcement learning, a novel Bellman equation and the augmented coupled game algebraic Riccati equation are presented to derived the optimal control policy for the augmented discrete-time Markov jump systems. Moreover, based on the augmented system, a newly constructed system is given to collect the input and output data, which solves the problem that the coupling term in the discrete-time Markov jump systems is difficult to solve. Subsequently, a novel model-free algorithm is designed that does not need the dynamic information of the original system. Finally, a numerical example is given to verify the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 00160032
- Volume :
- 360
- Issue :
- 18
- Database :
- Academic Search Index
- Journal :
- Journal of the Franklin Institute
- Publication Type :
- Periodical
- Accession number :
- 174419435
- Full Text :
- https://doi.org/10.1016/j.jfranklin.2023.10.008