Adaptive Optimal Output Regulation of Time-Delay Systems via Measurement Feedback
Document Type
Article
Publication Date
7-24-2018
Publication Title
IEEE Transactions on Neural Network and Learning Systems
DOI
10.1109/TNNLS.2018.2850520
ISSN
2162-2388
Abstract
This brief proposes a novel solution to problems related to the measurement feedback adaptive optimal output regulation of discrete-time linear systems with input time-delay. Based on reinforcement learning and adaptive dynamic programming, an approximate optimal control policy is obtained via recursive numerical algorithms using online information. Convergence proofs for the proposed algorithms are given. Notably, the exact knowledge of the plant and the exosystem is not needed. The learned control policy is only a function of retrospective input and measurement output data. Theoretical analysis and an application to a grid-connected inverter show that the proposed methodologies serve as effective tools for solving adaptive and optimal output regulation problems.
Recommended Citation
Gao, Weinan, Zhong-Ping Jiang.
2018.
"Adaptive Optimal Output Regulation of Time-Delay Systems via Measurement Feedback."
IEEE Transactions on Neural Network and Learning Systems, 30 (3): 938-945: IEEE Xplore.
doi: 10.1109/TNNLS.2018.2850520
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/127
