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Data-driven optimal cooperative tracking control for heterogeneous multi-agent systems
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AI
DOI：10.1016/j.isatra.2024.08.026.png)
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Abstract
En 中文
This paper presents a novel hierarchical control scheme for solving the data-driven optimal cooperative tracking control problem of heterogeneous multi-agent systems. Considering that followers cannot communicate with the leader, a prescribed-time fully distributed observer is devised to estimate the leader's state for each follower. Then, the data-driven decentralized controller is designed to ensure that the follower's output can track the leader's one. Compared with the existing results, the advantages of the designed distributed observer are that the prescribed convergence time is completely predetermined by the designer, and the design of the observer gain is independent of the global topology information. Besides, the advantages of the designed decentralized controller are that neither the follower's system model nor a known initial stabilizing control policy is required. Finally, simulation results exemplify the advantage of the proposed method.
Keywords:
Prescribed time
Reinforcement learning
Heterogeneous multi-agent systems
Fully distributed observer
Journal
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6.5
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5.9K
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