The invention discloses an automobile side slip angle observation method based on a fuzzy dynamics system. The method comprises the steps of 1, building a vehicle dynamics model according to the collected vehicle driving information, introducing an uncertainty parameter, and generating a side slip angle observation equation, and enabling the value of the uncertainty parameter to be described by afirst fuzzy set, 2, calculating a transient performance function and a steady-state performance function of the side slip angle observation equation, and calculating optimal adjustable parameters of the side slip angle observation equation through a dynamic game algorithm, the optimal adjustable parameters comprising a first adjustable parameter and a second adjustable parameter, and 3, accordingto the optimal adjustable parameters and the side slip angle observation equation, calculating a side slip angle observation value corresponding to the vehicle driving information. According to the technical scheme, modeling errors of an existing observation method based on a dynamic model are reduced, the accuracy of automobile side slip angle calculation is improved, and the overall performanceof the observer can be improved.
本申请公开了一种基于模糊动力学系统的汽车质心侧偏角观测方法,包括:步骤1,根据采集到的车辆行驶信息,构建车辆动力学模型,并引入不确定性参量,生成质心侧偏角观测方程,其中,不确定性参量的取值由第一模糊集合描述;步骤2,计算质心侧偏角观测方程的瞬态性能函数与稳态性能函数,通过动态博弈算法,计算质心侧偏角观测方程的最优可调参数,其中,最优可调参数包括第一可调参数和第二可调参数;步骤3,根据最优可调参数和质心侧偏角观测方程,计算车辆行驶信息对应的质心侧偏角观测值。通过本申请中的技术方案,降低了现有基于动力学模型观测方法在建模上的误差,提高了汽车质心侧偏角计算的准确性,且有助于提高观测器的整体性能。
Automobile side slip angle observation method based on fuzzy dynamics system
一种基于模糊动力学系统的汽车质心侧偏角观测方法
2020-11-20
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion / G06F ELECTRIC DIGITAL DATA PROCESSING , Elektrische digitale Datenverarbeitung / G06N COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS , Rechnersysteme, basierend auf spezifischen Rechenmodellen |
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