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Data-Driven Policy Iteration for Nonlinear Optimal Control Problems.

Authors :
Possieri C
Sassano M
Source :
IEEE transactions on neural networks and learning systems [IEEE Trans Neural Netw Learn Syst] 2023 Oct; Vol. 34 (10), pp. 7365-7376. Date of Electronic Publication: 2023 Oct 05.
Publication Year :
2023

Abstract

The design of optimal control laws for nonlinear systems is tackled without knowledge of the underlying plant and of a functional description of the cost function. The proposed data-driven method is based only on real-time measurements of the state of the plant and of the (instantaneous) value of the reward signal and relies on a combination of ideas borrowed from the theories of optimal and adaptive control problems. As a result, the architecture implements a policy iteration strategy in which, hinging on the use of neural networks, the policy evaluation step and the computation of the relevant information instrumental for the policy improvement step are performed in a purely continuous-time fashion. Furthermore, the desirable features of the design method, including convergence rate and robustness properties, are discussed. Finally, the theory is validated via two benchmark numerical simulations.

Details

Language :
English
ISSN :
2162-2388
Volume :
34
Issue :
10
Database :
MEDLINE
Journal :
IEEE transactions on neural networks and learning systems
Publication Type :
Academic Journal
Accession number :
35100122
Full Text :
https://doi.org/10.1109/TNNLS.2022.3142501