Output Consensus on Periodic References for Constrained Multi-agent Systems Under a Switching Network
It addresses a challenging control problem for multi-agent systems with periodic references and switching networks, offering a distributed solution that avoids global information.
This paper tackles the output consensus problem for constrained heterogeneous multi-agent systems tracking periodic references under switching networks. The proposed model predictive control method with an artificial reference achieves asymptotic consensus while maintaining constraint satisfaction, without requiring global information.
This work addresses the output consensus problem of constrained heterogeneous multi-agent systems under a switching network with potential communication delays, where outputs are periodic and characterized by an exosystem. Since periodic references have more complex dynamics, it is more challenging to track periodic references and achieve consensus on them. In this paper, a model predictive control method incorporating an artificial reference and a modified cost function is proposed to track periodic references, which maintains recursive feasibility even when references switch. Moreover, consensus protocols are proposed to achieve consensus on periodic references in different scenarios, in which global information such as the set of globally admissible references and the global time index are not involved. Theoretical analysis proves that constrained output consensus is asymptotically achieved with the proposed algorithm as the references of each agent converge and agents track their references while maintaining constraint satisfaction. Finally, numerical examples are provided to verify the effectiveness of the proposed algorithm.