期刊
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
卷 65, 期 4, 页码 1763-1770出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TAC.2019.2937500
关键词
Laplace equations; Multi-agent systems; Linear programming; Convergence; Symmetric matrices; Upper bound; Heuristic algorithms; Distributed convex optimization; event-triggered; multiagent systems
资金
- Science and Technology Facilities Council (STFC) [ST/N006852/1]
- Engineering and Physical Sciences Research Council (EPSRC) [EP/S019219/1]
- National Natural Science Foundation of China [61473005]
- EPSRC [EP/S019219/1] Funding Source: UKRI
- STFC [ST/N006852/1] Funding Source: UKRI
This note considers the distributed optimal coordination (DOC) problem for heterogeneous linear multiagent systems. The local gradients are locally Lipschitz and the local convexity constants are unknown. A control law is proposed to drive the states of all agents to the optimal coordination that minimizes a global objective function. By exploring certain features of the invariant projection of the Laplacian matrix, the global asymptotic convergence is guaranteed utilizing only local interaction. The proposed control law is then extended with event-triggered communication schemes, which removes the requirement for continuous communications. Under the event-triggered control law, it is proved that no Zeno behavior is exhibited and the global asymptotic convergence is preserved. The proposed control laws are fully distributed, in the sense that the control design only uses the information in the connected neighborhood. Furthermore, to achieve the DOC for linear multiagent systems with unmeasurable states, an observer-based event-triggered control law is proposed. A simulation example is given to validate the proposed control laws.
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