This paper presents a novel model-based observer algorithm to address issues associated with nonlinear suspension system state estimation using interacting multiple model unscented Kalman Filters (IMMUKF) under various road excitation. Due to the fact that practical working condition is complex for the suspension system, e.g. additional load. Meanwhile, the changed sprung mass parameter will induce model changed of suspension system, and it can lead to state transition between various models. To tackle the mentioned issue, the models of road profile and suspension system are first established to describe the nonlinear suspension dynamics. Then, considering the variation of sprung mass under various movement conditions, an unscented Kalman Filter (UKF) algorithm is proposed to identify the sprung mass. Based on the interacting multiple model (IMM) and Markov Chain Monte Carlo (MCMC) theory, a novel IMMUKF observer is developed to estimate the movement state of suspension system. The stability conditions for the proposed observer is calculated using the stochastic stability theory. Finally, simulations and validations are performed on a quarter vehicle suspension system under various ISO road excitations, to validate the UKF and IMMUKF algorithms for acquiring suspension system states, and results illustrate that the maximum root mean square error of state estimation for the proposed algorithm is less than 7.5 %.

本文提出了一种新的基于模型的观察器算法，以在各种道路激励下使用相互作用的多模型无迹卡尔曼滤波器 (IMMUKF) 解决与非线性悬架系统状态估计相关的问题。由于悬挂系统的实际工作条件是复杂的，例如附加负载。同时，变化的簧载质量参数会引起悬架系统的模型变化，并可能导致各种模型之间的状态转换。为了解决上述问题，首先建立了道路剖面和悬架系统模型来描述非线性悬架动力学。然后，考虑不同运动条件下簧载质量的变化，提出了一种无迹卡尔曼滤波器（UKF）算法来识别簧载质量。基于相互作用多重模型（IMM）和马尔可夫链蒙特卡罗（MCMC）理论，开发了一种新颖的IMMUKF观测器来估计悬架系统的运动状态。建议观测器的稳定性条件是使用随机稳定性理论计算的。最后，在各种 ISO 道路激励下对四分之一车辆悬架系统进行仿真和验证，以验证 UKF 和 IMMUKF 算法获取悬架系统状态，结果表明该算法的状态估计的最大均方根误差为小于 7.5%。