TY - GEN
T1 - Adaptive Optimal Output Regulation of Discrete-Time Linear Systems
T2 - 62nd IEEE Conference on Decision and Control, CDC 2023
AU - Chakraborty, Sayan
AU - Gao, Weinan
AU - Vamvoudakis, Kyriakos G.
AU - Jiang, Zhong Ping
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this paper, we solve the optimal output regulation problem for discrete-time systems without precise knowledge of the system model. Drawing inspiration from reinforcement learning and adaptive dynamic programming, a data-driven solution is developed that enables asymptotic tracking and disturbance rejection. Notably, it is discovered that the proposed approach for discrete-time output regulation differs from the continuous-time approach in terms of the persistent excitation condition required for policy iteration to be unique and convergent. To address this issue, a new persistent excitation condition is introduced to ensure both uniqueness and convergence of the data-driven policy iteration. The efficacy of the proposed methodology is validated by an inverted pendulum on a cart example.
AB - In this paper, we solve the optimal output regulation problem for discrete-time systems without precise knowledge of the system model. Drawing inspiration from reinforcement learning and adaptive dynamic programming, a data-driven solution is developed that enables asymptotic tracking and disturbance rejection. Notably, it is discovered that the proposed approach for discrete-time output regulation differs from the continuous-time approach in terms of the persistent excitation condition required for policy iteration to be unique and convergent. To address this issue, a new persistent excitation condition is introduced to ensure both uniqueness and convergence of the data-driven policy iteration. The efficacy of the proposed methodology is validated by an inverted pendulum on a cart example.
UR - http://www.scopus.com/inward/record.url?scp=85184817101&partnerID=8YFLogxK
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U2 - 10.1109/CDC49753.2023.10383803
DO - 10.1109/CDC49753.2023.10383803
M3 - Conference contribution
AN - SCOPUS:85184817101
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 7950
EP - 7955
BT - 2023 62nd IEEE Conference on Decision and Control, CDC 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 December 2023 through 15 December 2023
ER -