基于多模型迭代的车辆状态融合估计方法

Vehicle State Fusion Estimation Method Based on Multi-model Iteration

为了提高车辆行驶状态估计的可靠性,提出一种基于多模型观测器误差补偿与迭代的车辆状态融合估计方法。基于三自由度车辆动力学模型设计了车辆状态强跟踪滤波估计算法;同时,根据四轮轮速耦合关系,考虑到数据扰动和病态矩阵的影响,设计了车辆状态的岭估计算法。为进一步提高估计系统的可靠性,提出了动力学模型观测器与运动学模型观测器补偿与迭代的估计方式,设计了模糊控制器,根据实时的质心侧偏角和滑移率的伪量测值,判断强跟踪滤波器和岭估计器估计结果所占权重,利用闭环估计系统的迭代与融合提高估计性能。仿真和道路实验结果表明,所提出的车辆状态融合估计方法能够兼顾强跟踪滤波算法与岭估计算法的优势,根据车辆纵向滑移和质心侧偏角动态调节强跟踪估计与岭估计结果的权重系数,从而在保证估计精度的同时提高了估计系统的多工况适应能力。

In order to improve the reliability of vehicle running state estimation, a vehicle state fusion estimation method based on multi-model observer error compensation and iteration was proposed. A strong tracking filter estimation algorithm was presented for vehicle state estimation based on three-degree-of- freedom vehicle dynamics model, meanwhile, using the coupling relationship of four wheel speed, a ridge estimation algorithm for vehicle state estimation was designed considering the influence of data disturbance and ill-conditioned matrix. To further improve the reliability of estimation system, an estimation strategy with the error compensation and iteration between the dynamic-model-based observer and the kinematicmodel-based observer was developed, a fuzzy controller was designed which was used to judge the weight of strong tracking filter and ridge estimator according to the real-time pseudo measurement value of sideslip angle and longitudinal slip rate, and then estimation performance was improved by iteration and fusion of the closed-loop estimation system. The results of the simulation and road test showed that the proposed vehicle state fusion estimation method can integrate the advantages of strong tracking filter algorithm and ridge estimation algorithm, dynamically adjust the weight coefficients of strong tracking filter and ridge estimation results according to the vehicle longitudinal slip ratio and sideslip angle, guarantee the estimation accuracy and synchronously improve the adaptability of the estimation system under multiple conditions.