Entropy-aware event-triggered neural control for finite-time practical consensus of heterogeneous multi-agent systems under DoS attacks

This paper investigates the finite-time practical consensus problem for heterogeneous second-order multi-agent systems subject to denial-of-service attacks. An entropy-aware event-triggered neural control framework is proposed that integrates multidimensional entropy-based attack detection across temporal, spatial, and frequency domains, entropy-guided adaptive event-triggering mechanisms, and finite-time control augmented by radial basis function neural network compensation for unknown heterogeneous dynamics. Rigorous Lyapunov-based theoretical analysis establishes finite-time practical consensus with explicit settling-time bounds dependent on initial conditions while excluding Zeno behavior. Simulation results demonstrate that, under diverse attack patterns, the proposed method achieves consensus in 10.04 s (4.0% faster than resilient event-triggered control) with only 4672 transmissions (approximately 80.5% reduction), validating superior attack resilience and communication efficiency.