<p>This paper investigates leader-follower structures in robotic networks, which are highly susceptible to time delays, disturbances, and Denial-of-Service (DoS) attacks that can destabilize coordination and reduce system reliability. Existing control strategies often struggle to ensure robustness across different communication topologies, particularly when impulsive attacks interrupt network operations. To address these challenges, a neural network–based event-triggered control framework is proposed for both balanced and unbalanced signed network topologies in multi-layer robotic systems. The design integrates Lyapunov-based stability analysis to guarantee resilience against disturbances and DoS attacks, explicitly covering three impulsive attack phases–normal, attack, and recovery–under multiple triggering conditions. A distinctive feature of the proposed approach is the use of an adapted Lyapunov function in unbalanced structures, which enhances robustness in managing dynamic uncertainties. Numerical simulations on three-layer robotic networks validate the effectiveness of the strategy, demonstrating that the proposed controllers sustain stability and reliable performance even under severe adversarial conditions.</p>