Sub-optimal tracking in switched systems with fixed final time and fixed mode sequence using reinforcement learning

Authors

Tohid Sardarmehni, The University of Texas Rio Grande Valley
Xingyong Song, Texas A&M University

Document Type

Article

Publication Date

1-2021

Abstract

Approximate dynamic programming is used to solve optimal tracking problems in switched systems with controlled subsystems and fixed mode sequence. Two feedback control solutions are generated such that the system tracks a desired reference signal, and the optimal switching instants are sought. Simulation results are provided to illustrate the effectiveness of the solutions.

Comments

© 2020 Published by Elsevier B.V. Original published version available at https://doi.org/10.1016/j.neucom.2020.09.011 https://par.nsf.gov/biblio/10207508-sub-optimal-tracking-switched-systems-fixed-final-time-fixed-mode-sequence-using-reinforcement-learning

Recommended Citation

Sardarmehni, Tohid, and Xingyong Song. "Sub-optimal tracking in switched systems with fixed final time and fixed mode sequence using reinforcement learning." Neurocomputing 420 (2021): 197-209. https://doi.org/10.1016/j.neucom.2020.09.011

Publication Title

Neurocomputing

DOI

10.1016/j.neucom.2020.09.011