Event-Triggered Adaptive Neural Asymptotic Control for Hydraulic Active Suspension Units in Emergency Rescue Vehicles

This article addresses issues such as vehicle body instability, vibration, and model uncertainties in active suspension systems (ASSs) when vehicles traverse rough terrain, proposing an event-triggered adaptive neural asymptotic control (ETANAC) method with prescribed performance for the active suspension unit (ASU). The proposed method consists of prescribed performance control (PPC), adaptive neural backstepping asymptotic control, event-triggered control (ETC), Nussbaum technique, and the ASU model. The PPC method can rapidly adjust the vehicle body displacement to a stable state. The adaptive neural asymptotic control method with prescribed performance effectively reduces the amplitude of body vibration displacement and acceleration, ensuring that the tracking error asymptotically converges to zero. This method addresses the challenge of coordinating vehicle body displacement control and ride comfort. ETC can reduce the actuator triggering frequency. The Nussbaum technique addresses the issue of unknown control direction in the ASU. This article uses Barbalat’s lemma and Lyapunov methods to analyze the stability of the proposed method, proving the asymptotic convergence of all tracking errors. Finally, numerical simulation experiments are conducted to verify the effectiveness and superiority of the proposed method.