Novel Dynamic Instantaneous–Integral Fusion Event‐Triggered Control of Networked Non‐Linear Rail Vehicle Active Suspension

ABSTRACT This paper investigates dynamic instantaneous‐integration event‐triggered control for networked non‐linear rail vehicle suspension systems over the train communication network (TCN). The primary goal is to ensure the desired suspension performance while efficiently utilizing the TCN resources. Firstly, a 2‐DOF non‐linear model is developed, with the non‐linearities approximated by a neural network using a dissipativity‐learning method. To efficiently conserve TCN resources during signal transmission, this paper proposes a novel dynamic instantaneous‐integral fusion event‐triggered (DIFT) scheme. Compared with event‐triggered schemes based solely on instantaneous or accumulated state errors, the DIFT condition uses a fusion of the instantaneous error and the integral of the state error and compares it to a dynamic threshold that depends on the sampled state at the triggering instant and an auxiliary dynamic variable. This integrated approach ensures robust, Zeno‐free event‐triggered updates while maintaining desired control performance and communication efficiency. Furthermore, stability and dissipativity conditions based on a looped Lyapunov function are proposed to guarantee the stability and dissipative performance of the closed‐loop suspension system. Then, the desired controller, the event‐triggered parameter and the updating law for weight are co‐designed. Simulation experiments are conducted to validate the effectiveness of the proposed method.