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Dynamic Modeling and State Estimation for Multi-In-Wheel-Motor-Driven Intelligent Vehicle

机译:多轮内电动机驱动智能车辆的动态建模与状态估计

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摘要

Dynamic modeling and state estimation are significant in the trajectory tracking and stability control of the intelligent vehicle. In order to meet the requirement of the stability control of the eight-in-wheel-motor-driven intelligent vehicle, a full vehicle dynamics model with 12 degrees of freedom, including the longitudinal, lateral, yaw and roll motion of the body, and rotational motion of 8 wheels, is established for the research of the intelligent vehicle in this paper. By simulation with MATLAB/SIMULINK and by comparison with the TruckSim software, the reliability and practicality of the dynamics model are verified. Based on the established dynamics model, an extended Kalman filter (EKF) state observer is proposed to estimate the vehicle sideslip angle, roll angle and yaw rate, which are the key parameters to the stability control of the intelligent vehicle. The accuracy and effectiveness of the EKF state observer are evaluated and validated through co-simulation between MATLAB/ SIMULINK and TruckSim. The results show the proposed EKF observer can effectively filter the noise and has high accuracy in estimating the vehicle sideslip angle, roll angle and yaw rate.
机译:动态建模和状态估计在智能车辆的轨迹跟踪和稳定性控制中是显着的。为了满足八轮电动机驱动智能车辆的稳定控制要求,具有12度自由的全车动力学模型,包括纵向,横向,横摆和滚动运动,以及建立了8个轮子的旋转运动,用于研究本文的智能车辆。通过使用MATLAB / SIMULINK进行仿真,并通过与TruckSIM软件进行比较,验证了动力学模型的可靠性和实用性。基于已建立的动态模型,提出了一个扩展的卡尔曼滤波器(EKF)状态观察者来估计车辆侧线角,滚动角度和横摆率,这是智能车辆稳定控制的关键参数。通过Matlab / Simulink和Trucksim之间的共模进行评估和验证EKF状态观察者的准确性和有效性。结果表明,所提出的EKF观察者可以有效地过滤噪声并在估计车辆侧线角度,滚动角度和横摆率时具有高精度。

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