Leader-Following Consensus of Discrete-Time Multi-Agent Systems via Distributed Model Reference Adaptive Control

TY - GEN

T1 - Leader-Following Consensus of Discrete-Time Multi-Agent Systems via Distributed Model Reference Adaptive Control

AU - Xu, Yuchun

AU - Zhang, Yanjun

AU - Zhang, Ji Feng

N1 - Publisher Copyright: © 2025 Technical Committee on Control Theory, Chinese Association of Automation.

PY - 2025

Y1 - 2025

N2 - In this paper, we study the leader-following adaptive tracking control problem for a class of discrete-time linear uncertain multi-agent systems (MAS). To deal with this problem, we develop a distributed model reference adaptive control framework, which extends the classical MRAC method to MAS scenario. Taking the reference model system as the leader agent, we first develop a nominal distributed controller only using local neighboring information, where the ideal controller parameters are derived by a matching equation and neighboring system parameters. Based on the nominal controller structure, a distributed adaptive control law is developed by introducing a parameter update law for the estimated controller parameters. Further, to eliminate the requirement for the prior knowledge of control gains, we devise a modified distributed adaptive control law, which incorporate the implicit control gain estimate into the controller structure, thus deriving a linear local estimation error equation. Thus, a gradient based parameter update law could be designed without any prior parameter information. Notably, the leader agent is also with paraemter uncertainty and the modification does not rely on common Nussbaum function. Asymptotical leader-following output tracking is achieved under the proposed distributed adaptive control strategies. The simulation results also demonstrate the theoretical findings.

AB - In this paper, we study the leader-following adaptive tracking control problem for a class of discrete-time linear uncertain multi-agent systems (MAS). To deal with this problem, we develop a distributed model reference adaptive control framework, which extends the classical MRAC method to MAS scenario. Taking the reference model system as the leader agent, we first develop a nominal distributed controller only using local neighboring information, where the ideal controller parameters are derived by a matching equation and neighboring system parameters. Based on the nominal controller structure, a distributed adaptive control law is developed by introducing a parameter update law for the estimated controller parameters. Further, to eliminate the requirement for the prior knowledge of control gains, we devise a modified distributed adaptive control law, which incorporate the implicit control gain estimate into the controller structure, thus deriving a linear local estimation error equation. Thus, a gradient based parameter update law could be designed without any prior parameter information. Notably, the leader agent is also with paraemter uncertainty and the modification does not rely on common Nussbaum function. Asymptotical leader-following output tracking is achieved under the proposed distributed adaptive control strategies. The simulation results also demonstrate the theoretical findings.

KW - adaptive control

KW - control gain uncertainty

KW - Leader-following tracking

KW - multi-agent system

UR - https://www.scopus.com/pages/publications/105020283793

U2 - 10.23919/CCC64809.2025.11178385

DO - 10.23919/CCC64809.2025.11178385

M3 - Conference contribution

AN - SCOPUS:105020283793

T3 - Chinese Control Conference, CCC

SP - 2837

EP - 2842

BT - Proceedings of the 44th Chinese Control Conference, CCC 2025

A2 - Sun, Jian

A2 - Yin, Hongpeng

PB - IEEE Computer Society

T2 - 44th Chinese Control Conference, CCC 2025

Y2 - 28 July 2025 through 30 July 2025

ER -