Game-Based Dynamic Event-Driven Optimal Bipartite Consensus Control for Multi-Agent Systems
Abstract: This work mainly focuses on the dynamic event-driven optimal bipartite consensus control problem for nonlinear multi-agent systems (MASs) considering false data injection (FDI) attacks under signed topology. First, in view of the opposed relationship between attacker and defender, the optimal bipartite consensus control problem for MASs under FDI attacks is transformed into a zero-sum game problem. Under the unified design framework of zero-sum game and adaptive dynamic programming theory, the performance function is built to provide the foundation to obtain Nash equilibrium point. Subsequently, to reduce network resource consumption, the dynamic event-driven-based Hamilton-Jacobi-Isaacs (HJI) equation along with the designed dynamic event-driven mechanism is derived. Together with the critic neural network and gradient descent method, the solution to the dynamic event-driven-based HJI equation is learned. Moreover, both the boundedness of all signals in the closed-loop systems and the avoidance of Zeno behavior are proved. Finally, the simulation results confirm the validity of the designed control scheme.
Submission Number: 232
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