Data-driven cooperative optimal output regulation for linear discrete-time multi-agent systems by online distributed adaptive internal model approach

In this study, a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy, which solves the cooperative optimal output regulation problem for linear discrete-time multi-agent systems. Notably, the dynamics of all the agent systems and exo-system is...

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Bibliographic Details
Published in:Science China. Information sciences 2023-07, Vol.66 (7), p.170202, Article 170202
Main Authors: Xie, Kedi, Jiang, Yi, Yu, Xiao, Lan, Weiyao
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Computer Science
Control systems design
Controllers
Convergence
Cooperative control
Design techniques
Discrete time systems
Distance learning
Dynamic programming
Information Systems and Communication Service
Machine learning
Multiagent systems
Optimal control
Research Paper
Stability analysis
Teaching methods
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Summary:In this study, a data-driven learning algorithm was developed to estimate the optimal distributed cooperative control policy, which solves the cooperative optimal output regulation problem for linear discrete-time multi-agent systems. Notably, the dynamics of all the agent systems and exo-system is completely unknown. By combining adaptive dynamic programming with an internal model, a model-free off-policy learning method is proposed to estimate the optimal control gain and the distributed adaptive internal model by only accessing the measurable data of multi-agent systems. Moreover, different from the traditional cooperative adaptive controller design method, a distributed internal model is approximated online. Convergence and stability analyses show that the estimate controller generated by the proposed data-driven learning algorithm converges to the optimal distributed controller. Finally, simulation results verify the effectiveness of the proposed method.
ISSN:1674-733X
1869-1919
DOI:10.1007/s11432-022-3687-1