IMU-based estimation and control strategy design of electronically controlled QZS air suspension system

This paper presents a novel estimation and control framework for Quasi-Zero-Stiffness (QZS) air suspension systems based on Inertial Measurement Unit (IMU) data. A combined algorithm integrating Grey Wolf Optimisation (GWO) and Unscented Kalman Filtering (UKF) is proposed to address the challenges of noise and uncertainty inherent in IMU signals. First, the mathematical model of the QZS air suspension system is formulated, and an H2 controller coupled with an IMU simulation model is developed. IMU measurements are incorporated into the state vector directly, while the remaining state variables are estimated through the GWO-UKF algorithm. To further improve estimation adaptability and accuracy, GWO is employed to optimise the UKF parameters. Simulation results demonstrate that the proposed method achieves high accuracy in estimating the unsprung mass velocity and dynamic displacement. Finally, Hardware-in-the-Loop (HiL) experiments confirm that the proposed controller significantly enhances vehicle ride comfort and operational stability.