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<p>As a crucial component of intelligent chassis systems, air suspension significantly enhances driver comfort and vehicle stability. To further improve the adaptability of commercial vehicles to complex and variable road conditions, this paper proposes a linear motor active suspension with quasi-zero stiffness (QZS) air spring system. Firstly, a dynamic model of the linear motor active suspension with QZS air spring system is established. Secondly, considering the random uncertainties in the linear motor parameters due to manufacturing and environmental factors, a dynamic model and state equations incorporating these uncertainties are constructed using the polynomial chaos expansion (PCE) method. Then, based on H<sub>2</sub> robust control theory and the Kalman filter, a state feedback control law is derived, accounting for the random parameter uncertainties. Finally, simulation and hardware-in-the-loop (HIL) experimental results demonstrate that the PCE-H<sub>2</sub> robust controller not only provides better performance in terms of vehicle ride comfort compared to general H<sub>2</sub> robust controller but also exhibits higher robustness to the effects of random uncertain parameters, resulting in more stable control performance.</p>

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Modeling and Control of the Linear Motor Active Suspension with Quasi-zero Stiffness Air Spring System Using Polynomial Chaos Expansion

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月23日

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  • Pai Li,
  • Xing Xu,
  • Cong Liang,
  • Te Chen,
  • Jiachen Jiang,
  • Vincent Akolbire Atindana

摘要

As a crucial component of intelligent chassis systems, air suspension significantly enhances driver comfort and vehicle stability. To further improve the adaptability of commercial vehicles to complex and variable road conditions, this paper proposes a linear motor active suspension with quasi-zero stiffness (QZS) air spring system. Firstly, a dynamic model of the linear motor active suspension with QZS air spring system is established. Secondly, considering the random uncertainties in the linear motor parameters due to manufacturing and environmental factors, a dynamic model and state equations incorporating these uncertainties are constructed using the polynomial chaos expansion (PCE) method. Then, based on H2 robust control theory and the Kalman filter, a state feedback control law is derived, accounting for the random parameter uncertainties. Finally, simulation and hardware-in-the-loop (HIL) experimental results demonstrate that the PCE-H2 robust controller not only provides better performance in terms of vehicle ride comfort compared to general H2 robust controller but also exhibits higher robustness to the effects of random uncertain parameters, resulting in more stable control performance.