Vehicle Motion Control with Jerk Constraint by Nonlinear Model Predictive Control

Kyoshiro Itakura
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Abstract

In this paper, a nonlinear model predictive control considering vehicle jerk dynamics is proposed for improving ride comfort of passengers. Since the vehicle model in prediction phase requires high accuracy dynamics in order to handle the jerk motion, the approximated wheel load transfer dynamics is introduced. Also, to obtain the control capability for not only jerk but also acceleration, velocity and position, the expanded state space model including these dimensions into the one has been developed. It improves the utility as autonomous vehicle controller. By numerical simulation in assuming cornering driving scene, the effectiveness that jerk and other vehicle states enable to constraint simultaneously by individual torque distribution by electric power train is validated. Further, the principle of the optimized torque distribution by proposed method is analyzed.
基于非线性模型预测控制的车辆运动控制
为了提高乘客的乘坐舒适性,本文提出了一种考虑车辆抖动动力学的非线性模型预测控制方法。由于预测阶段的车辆模型需要较高的动力学精度来处理突跳运动,因此引入了近似的轮载传递动力学。同时,为了获得系统对加速度、速度和位置的控制能力,建立了包括加速度、速度和位置在内的扩展状态空间模型。提高了自动驾驶汽车控制器的实用性。通过数值模拟,验证了在假设转弯驾驶场景下,电动传动系统的个别转矩分配同时约束车辆制动和其他状态的有效性。进一步分析了该方法优化转矩分配的原理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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