期刊
IEEE-CAA JOURNAL OF AUTOMATICA SINICA
卷 9, 期 3, 页码 520-532出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JAS.2021.1004359
关键词
Asymmetric input-constrained; heterogeneous nonlinear multiagent systems (MASs); Hamilton-Jacobi-Bellman (HJB) equation; novel observer; reinforcement learning (RL)
资金
- National Natural Science Foundation of China [61873300, 61722312]
- Fundamental Research Funds for the Central Universities [FRF-MP-20-11]
- Interdisciplinary Research Project for Young Teachers of University of Science and Technology Beijing (Fundamental Research Funds for the Central Universities) [FRFIDRY-20-030]
This paper considers the asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multi-agent systems. By performing a state-space transformation and designing a novel distributed observer, the satisfaction of asymmetric input constraints is guaranteed. With the help of a network of augmented systems and a data-based off-policy reinforcement learning algorithm, the constrained Hamilton-Jacobi-Bellman equation is solved. Simulation results demonstrate the correctness and validity of the theoretical results.
The asymmetric input-constrained optimal synchronization problem of heterogeneous unknown nonlinear multiagent systems (MASs) is considered in the paper. Intuitively, a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system. Then, considering that the leader's information is not available to every follower, a novel distributed observer is designed to estimate the leader's state using only exchange of information among neighboring followers. After that, a network of augmented systems is constructed by combining observers and followers dynamics. A nonquadratic cost function is then leveraged for each augmented system (agent) for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman (HJB) equation is solved in a data-based fashion. More specifically, a data-based off-policy reinforcement learning (RL) algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents' dynamics. Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated. Finally, the correctness and validity of the theoretical results are demonstrated by a simulation example.
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