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A Multi-Objective Model Predictive Control for Vehicle Adaptive Cruise Control System Based on a New Safe Distance Model
International Journal of Automotive Technology ( IF 1.6 ) Pub Date : 2021-04-02 , DOI: 10.1007/s12239-021-0044-0
Zhenze Liu , Qing Yuan , Guangming Nie , Yantao Tian

In order to be acceptable to drivers, the adaptive cruise control (ACC) systems need to be designed based on the analysis of human driver driving behavior. A new safe distance model is proposed in this paper based on the analysis of real-world driving test data. The goal of the control algorithm is to achieve naturalistic behavior of the vehicle that can comprehensively address the issue of driving safety, tracking performance, fuel economy and ride comfort. Firstly, a hierarchical control architecture is utilized which lower controller compensates for nonlinear vehicle dynamics and enables tracking of the desired acceleration. Then the acceleration rate of the following vehicle is introduced as state variables in the model of the car-following system for a more realistic and comprehensive description of dynamic evolution between the preceding car and the following car. Next, the control objectives above are formulated into a constrained quadratic programming problem under the framework of model predictive control (MPC). Finally, the simulation results show that the control strategy proposed in this paper can provide natural following performance that not only can satisfy driving safety, tracking performance but also can achieve fuel economy and ride comfort.



中文翻译:

基于新安全距离模型的车辆自适应巡航控制系统的多目标模型预测控制

为了使驾驶员能够接受,需要基于对人类驾驶员驾驶行为的分析来设计自适应巡航控制(ACC)系统。在对现实驾驶测试数据进行分析的基础上,提出了一种新的安全距离模型。控制算法的目标是实现车辆的自然行为,从而能够全面解决驾驶安全,跟踪性能,燃油经济性和乘坐舒适性的问题。首先,采用了分级控制架构,该架构的下级控制器补偿了非线性车辆动态特性,并能够跟踪所需的加速度。然后,将跟随车辆的加速度作为状态变量引入到跟随车辆系统的模型中,以便更现实,更全面地描述先前车辆和后续车辆之间的动态演变。接下来,在模型预测控制(MPC)的框架下,将上述控制目标表述为约束二次规划问题。最后,仿真结果表明,本文提出的控制策略可以提供自然的跟随性能,不仅可以满足行驶安全性,跟踪性能,还可以实现燃油经济性和乘坐舒适性。

更新日期:2021-04-04
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