Adaptive event-triggered quantized control for active suspension

Abstract

Aiming at the coexisting problems of time-varying transmission delay and signal quantization error in networked active suspensions, this paper proposes an L2 control method integrating dual-end quantization and adaptive event-triggered mechanism to simultaneously optimize vehicle ride performance and in-vehicle network resource utilization. This paper adopts a two-degree-of-freedom active suspension dynamic model that incorporates time-varying transmission delay characteristics, and dual-end quantization of states and control inputs is established, with the sector bound method adopted to handle quantization uncertainty. An adaptive event-triggered mechanism with boundary constraints is designed to dynamically adjust trigger thresholds and reduce redundant transmissions. Sufficient conditions are derived for the suspension system's global asymptotic stability with guaranteed L2 anti-disturbance performance. Adopting the L2 state-feedback control law and combining with solved event-triggered parameters achieves collaborative optimization of control performance and communication efficiency. As demonstrated by simulation tests, the newly proposed control strategy reduces peak body acceleration by approximately 65% and peak suspension deflection by approximately 70% under bump road impact; under random road excitation, vibration amplitudes of all core performance indicators are continuously and effectively suppressed. While improving ride comfort and driving safety, this method significantly reduces network communication load, offering an effective solution to communication-constrained active suspension systems.