基于模型预测控制的纵向驾驶辅助试验研究

H. Okuda, Y. Tazaki, Tatsuya Suzuki
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引用次数: 2

摘要

提出了一种基于模型预测控制(MPC)和连续/离散驾驶行为混合动力系统模型的个性化驾驶辅助系统。首先,将驾驶行为识别为分段ARX模型。然后,将其显式嵌入到寻找最优辅助输出的优化问题中。由于驾驶行为包含一些二元变量,因此将优化问题表述为混合整数规划问题。特别讨论了适应形势变化的一些适应机制。最后,用实际车辆对该方案进行了验证,实现了基于MPC的实时辅助控制。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An experimental study on longitudinal driving assistance based on model predictive control
This paper presents a novel personalized driver assistance system(PDAS) based on the model predictive control(MPC) together with a continuous/discrete hybrid dynamical system model of the driving behavior. First of all, the driving behavior is identified as the piecewise ARX model. Then, it is explicitly embedded in the optimization problem for finding the optimal assisting output. Since the driving behavior includes some binary variables, the optimization problem is formulated as the mixed integer programming. Some adaptation mechanism to accommodate to the change of the situation is particularly discussed. Finally, the proposed scheme is tested by using the real vehicle wherein the real-time assisting control based on MPC is implemented.
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