Sampled-Data Event-Triggered Robust Control for Active Suspensions of Electric Vehicles With Anti-Roll Performance and Reduced Communication Overhead Under Heterogeneous Excitations

This study introduces a sampled-data, event-triggered robust control framework to mitigate performance degradation and communication overhead in active suspension systems with advanced-dynamic-damper-motor (ADM) arising from increased unsprung mass and conventional time-triggered scheduling for electric vehicles. First, we develop an event-triggered sampling strategy that substantially reduces communication while enforcing a strictly positive minimum event-trigger interval equal to the data sampling time, thereby precluding Zeno behavior. Second, we derive robustD-stability conditions with a certifiedL2-gain from external disturbances to suspension states bounded by γ, ensuring convergence of consensus errors among suspensions subjected to heterogeneous excitations to improve vehicle anti-roll performance. Hardware-in-the-loop (HIL) experiments under random-road and bumpimpulse profiles demonstrate that the proposed event-triggered controller achieves up to 44.4% RMS reduction in sprung-mass acceleration and 36–46% improvement in suspension deflection relative to passive suspension, while providing 12–24% additional RMS improvement compared with a baseline active controller. Moreover, the event-triggered mechanism reduces communication load by up to 23% under random excitation and 10.7% under impulse disturbance compared with 100 Hz periodic transmission.