汽车ACC系统可变输出论域模糊控制算法*

基于模糊控制理论设计了自适应巡航控制（ACC）分层控制器,并根据车辆的实际行驶特性确定ACC节气门调节模式和制动干预模式的切换条件,设计了ACC可变输出论域模糊控制算法,并进行了ACC典型工况的仿真。仿真结果表明控制算法满足乘坐舒适性和保持安全车距的要求。     

Design and evaluation of a model predictive vehicle control algorithm for automated driving using a vehicle traffic simulator

This paper describes the design and evaluation of a model predictive control algorithm for automated driving on a motorway using a vehicle traffic simulator. For the development of a highly automated driving control algorithm, motion planning is necessary to satisfy driving condition in various road traffic situations. There are two key issues in motion planning of automated driving vehicles. One of the key issues is how to handle potentially dangerous situations that could occur in order to guarantee the safety of vehicles. The second key issue is how to guarantee the disturbance rejection of the controller under model uncertainties and external disturbances. To improve safety with respect to the future behaviors of subject vehicles, not the current states but rather the predicted behaviors of surrounding vehicles should be considered. The desired driving mode and a safe driving envelope are determined based on the probabilistic prediction of surrounding vehicles behaviors over a finite prediction horizon. To obtain the desired steering angle and longitudinal acceleration for maintaining the subject vehicle in the safe driving envelope during a finite prediction horizon, a motion planning controller is designed based on an model predictive control (MPC) approach. The developed control algorithm has been successfully implemented on a vehicle electronic control unit (ECU). The proposed control algorithm has been evaluated on a real-time vehicle traffic simulator. The throttle, brake, and steering control inputs and the controlled vehicle behavior have been compared to those of manual driving.     

Shared Control of Highly Automated Vehicles Using Steer-By-Wire Systems

A shared control of highly automated Steer-by-Wire system is proposed for cooperative driving between the driver and vehicle in the face of driver's abnormal driving. A fault detection scheme is designed to detect the abnormal driving behaviour and transfer the control of the car to the automatic system designed based on a fault tolerant model predictive control (MPC) controller driving the vehicle along an optimal safe path. The proposed concept and control algorithm are tested in a number of scenarios representing intersection, lane change and different types of driver's abnormal behaviour. The simulation results show the feasibility and effectiveness of the proposed method.      

一种用于AFS的半实物仿真平台

为了给自适应前照灯系统（Adaptive Front-Lighting System,AFS）提供一个控制算法验证平台,开发了一套基于汽车驾驶模拟器的AFS半实物硬件仿真平台。在该仿真平台中,PC机采集驾驶模拟器的档位、油门、离合、刹车、方向盘等驾驶信息,由车辆动力学模型模拟实际车辆行为,采用AFS控制模型计算车灯转角控制量,并发送至电机驱动模块进而控制车灯转动,同时将车灯转角信息反馈至PC机。实验表明,该系统能实时记录和显示AFS工作过程中的多种参数,便于进行实验观察和数据分析,从而为AFS系统的控制算法提供一个检验和修正的平台。     

基于横纵向联合控制的多目标优化车辆跟驰研究

为解决车辆在拥堵环境中因车速波动较大所带来的跟驰平稳性较差、跟踪无效或不安全等问题,提出了基于车辆模型和深度强化学习的多目标优化跟驰方案。首先基于车辆横纵向动力学建立车辆跟驰模型,然后根据车间距误差、速度误差、横向偏差及相对偏航角等,利用深度确定性策略梯度算法得到跟驰车的加速度和转向角,以更平稳安全地控制跟驰车辆。经NGSIM公开驾驶数据集进行测试与验证,该方案可有效地提升跟驰车辆的稳定、舒适与安全性,对保证交通安全和提升道路通行能力具有重要意义。     

Fuzzy Longitudinal Controller Design and Experimentation for Adaptive Cruise Control and Stop&;Go

This paper presents a fuzzy longitudinal control system with car-following speed ranging from 0 to 120 km/h, thereby achieving the main functions of both adaptive cruise control (ACC) and Stop&Go control. A fuzzy longitudinal controller is synthesized by inputting the difference of the actual relative distance and the safe distance obtained from the preceding vehicle, and the relative speed, and then outputting the pulse-width-modulation (PWM) signal to control the output forces of the vacuum boosters. With the use of the high-level controller from dSPACE, the fuzzy control law is easily and rapidly implemented using Matlab/Simulink for the experimental car, and the controller’s parameters can be changed and updated by analyzing data based on the relative distance using Lidar, the speed of the host vehicle, the opening of the throttle and the position of the braking pedal. For the sake of safe driving, experimental results are conducted by simulating the various possible car-following conditions for the ACC and Stop&Go controllers, thereby obtaining virtually relative distances and speeds to tune the controller’s parameters and ensure the safety of the controller. Several car following experiments are conducted to show that the proposed fuzzy longitudinal controller is capable of achieving the requirements of comfort and safety, and giving a satisfactory performance at high and low speed conditions.     

Plug_in电动车能量管理控制器研究

基于模糊神经网络算法研究设计Plug_in混合动力汽车整车能量管理控制器。将驾驶行为用神经网络进行建模,驾驶模式、踏板(油门和刹车)位置以及当前车轮力矩作为神经网络输入,目标力矩作为输出;将道路类型、目标力矩、电池SOC、当前车轮力矩为模糊输入变量,以满足整车动力性能、燃油经济性和极限边界极值为约束条件,对混合动力汽车的能量进行分配与管理,并在DSP硬件平台设计实现能量管理控制器。测试表明,行驶里程在40 km内时,样车等价燃油经济性最好,随着行驶里程的增加,燃油经济性下降,整个测试过程中样车动力性能以及各部件工况良好。     

Vehicle longitudinal control using throttles and brakes

One of the main challenges of automotive vehicle longitudinal control is integrating throttle and brake control. The difficulties are mainly due to the necessity of switching throttles and brakes, and the non-symmetry of time delays - large delay for brakes, and small delay for throttles. Existing control techniques switch between throttles and brakes based on a simple predetermined criterion. As a result, switchings are frequent, and ride jerky. In this paper, we propose a control strategy where two control laws, one for the throttle and one for the brake, are computed simultaneously to optimize a certain tracking criterion by a learning algorithm. The two computed control signals are then used to determine whether to activate the throttle, or the brake, or inaction. The resulting control law is shown to give dramatically smoother behaviors for vehicle tracking.     

基于单片机的汽车电子油门控制器的设计与实现

通过对油门控制器技术的分析,结合其发展应用现状,设计了针对汽车油门开度调节,以及油门与刹车自动切换的电子油门控制器,并对汽车的智能控制进行了设计研究。通过对汽车发动机建立数学模型,分析了以单片机为核心的硬件设计原理和软件控制算法如何采用达林算法解决其时变滞后的问题。     

Design,tuning, and evaluation of a full-range adaptive cruise control system with collision avoidance

This paper describes the design, tuning, and evaluation of a full-range adaptive cruise control (ACC) system with collision avoidance (CA). The control scheme is designed to improve drivers’ comfort during normal, safe-driving situations and to completely avoid rear-end collision in vehicle-following situations. Driving situations are divided into safe, warning, and dangerous modes. Three different control strategies have been proposed, depending on the driving situation. The driving situations are determined using a non-dimensional warning index and the time-to-collision (TTC). The control parameters of the proposed ACC/CA system are tuned by a confusion-matrix method using manual-driving data in no-crashing driving situations. The vehicle-following characteristics of the subject vehicle were compared to real-world, manual-driving data. Finally, the ACC/CA system was also implemented in a real vehicle and tested in both safe-traffic and severe-braking situations. It is shown that the proposed control strategy can provide natural following performance that is similar to human manual-driving in both high-speed driving and low-speed stop-and-go situations. Furthermore, it can prevent the vehicle-to-vehicle distance from dropping to an unsafe level in a variety of driving conditions.     

利用试验数据控制汽车模型驾驶行为

在汽车性能仿真计算中,对汽车模型进行驾驶控制是其中关键的一个环节,直接影响仿真的计算结果。该文通过研究ADAMS／Car仿真软件中驾驶控制机理,讨论了其中转向、油门、离合、制动、和档位五个变量的控制方式和相关驾驶控制文件的编写。并针对原有控制中易于出现的问题,以试验数据为目标,通过PID算法对汽车模型的相应输入量进行控制,使仿真计算结果更为可信。利用试验数据对整车模型进行驾驶控制,对提高车辆系统的仿真计算水平有一定的意义。     

车辆自适应巡航控制策略研究与仿真

为了保证车辆自适应巡航控制(ACC)系统的安全性、跟踪效果和稳定性,基于车辆安全距离模型建立了车辆自适应巡航控制模型,在Trucksim中搭建CACC跟驰控制系统;通过Simulink与Trucksim联合仿真,验证系统的可行性.仿真结果表明,在车辆编队的行驶过程中,前后车的车距始终在设定的安全距离范围内波动,具有良好...     

Longitudinal control of an intelligent vehicle using particle swarm optimization based sliding mode control

This paper focuses on the design of longitudinal controller for an intelligent vehicle which was built at Asian Institute of Technology based on sliding mode control. The proposed controller uses particle swarm optimization (PSO) for optimal tuning of sliding surface and controller gain in the sliding mode controller (SMC). The longitudinal control is conducted via controlling of throttle value angle using PSO-based SMC on the simplified first-order linear model of the intelligent vehicle and controlling of brake force using fuzzy logic. In order to achieve the desired headway time, integration of throttle valve angle control and brake force control is required. To obtain the optimal parameters of SMC, two equations velocity updating and position updating are applied. Firstly, the performance of proposed controller is evaluated by using MATLAB simulation to compare with conventional PD controller. Finally, the experimental results show that the proposed PSO-based SMC can perform efficiently in longitudinal control of the intelligent vehicle.     

无级变速汽车的自适应模糊控制研究

为提高无级变速汽车控制系统对外界负荷扰动和各种运行工况的自适应能力,本文在简单的无级变速系统模型的基础上,设计了无级变速汽车的自适应模糊控制系统.所设计的自适应模糊控制系统由参数自调节油门模糊控制器和混合型模糊-PID的无级变速器速比和制动控制器构成,该控制系统只与汽车的瞬时运行状态有关而与汽车的参数无关,因此它对不确定的汽车参数和外界负荷的扰动具有良好的鲁棒性.仿真结果表明所设计的模糊控制系统具有良好的自适应能力.     

时延MPC自主车辆协同控制算法与仿真

协同自适应巡航控制（CACC）系统中车辆纵向运动的上下位分层控制器结构,上位控制器采用状态空间模型预测控制算法,利用期望距离以及车辆与环境的实时信息决策出被控车辆运动的期望加速度。下位控制器根据期望加速度,求解发动机节气门开度或制动压力。车辆的执行器时延会对系统的稳定性产生很大的影响。根据动态矩阵控制算法对纯滞后对象的补偿作用,提出一种改进的模型预测控制算法,并与PID控制算法（下位控制器）相结合形成自主车辆纵向运动的上下位分层控制器,以补偿车辆的执行器时延带来的影响。通过SIMULINK/CARSIM联合仿真平台对所设计的算法进行了仿真研究,仿真结果表明所设计算法减小了CACC系统车辆在跟随过程中的速度跟踪误差以及间距误差,提高了系统的稳定性法。     

基于Eclipse平台的车辆自适应巡航控制仿真

自适应巡航控制是一种先进的汽车辅助驾驶系统,可以减轻驾驶员的工作量并且可以提高驾驶的便利性和安全性。目前一般都是通过MatLab仿真曲线来检验车辆自适应巡航控制算法的控制效果,但该方式不够直观形象。基于Eclipse平台,用JAVA语言搭建一个具有动态效果的车辆巡航控制仿真平台。该平台可以模拟多种典型驾驶工况,直观有效地展现车辆自适应巡航控制算法的结果。最后,在该仿真平台上设计最优PD控制算法和智能驱动驾驶（IDM）控制算法验证车辆自适应巡航控制结果。结果表明,该Eclipse仿真平台能够有效地模拟多种驾驶工况,且能够直观有效地验证多种车辆自适应巡航控制算法的结果。     

模糊PID控制器在自适应巡航控制系统中的应用

车辆的油门控制算法是自适应巡航控制系统(ACC：Adaptive Cruise Control)中的核心算法之一。这里采用的模糊PID算法(Fuzzy PID)是对常用的两输入单输出模糊控制算法的改进。通过使用Matlab／Simulink建立车辆动力学模型和控制器模型,对控制器的性能进行了仿真分析,验证了算法的合理性。     

网联车辆自适应巡航控制算法验证平台设计

自适应巡航控制系统是一种车辆高级辅助驾驶系统，不仅可以减轻驾驶员工作负担，还能增强车辆的行车安全和驾乘舒适性。基于MATLAB和Python混合编程，在网络协议环境下设计智能车巡航控制算法验证平台；该平台能够模拟多种不同典型行车场景，其实时的信息采集与动画演示功能能够直观有效地展现车辆状态。结合所开发的平台软件，设计出增量式模型预测控制器（IMPC）和智能驱动驾驶（IDM）控制器，所提出的IMPC算法不仅能综合考虑网联车巡航多目标的特点，同时还满足巡航系统快速性和准确性的要求。最后结合典型驾驶工况，开展智能车的车辆自适应巡航控制实验。实验结果表明，基于MATLAB和Python混合编程的软件系统能有效模拟各种驾驶情景，并能结合智能车验证该自适应巡航控制算法的结果。     

基于MC9S08GB60的汽车电控悬架控制系统设计

对空气悬架的控制是提高车辆行驶平顺性的有效方法。以MC9S08GB60为控制芯片,以YBL6891H型客车的1/4车辆模型为研究对象,设计了一种客车用空气悬架的电子控制单元。该控制单元通过调整空气弹簧的刚度,降低车身垂直加速度,并采用模糊PID控制算法,提高控制精度。试验表明:该电子控制单元能有效降低车身垂直加速度,在改善车辆行驶平顺性的同时,提高了操纵稳定性。     

基于强化学习的多目标车辆跟随决策算法

为满足自适应巡航系统跟车模式下的舒适性需求并兼顾车辆安全性和行车效率,解决已有算法泛化性和舒适性差的问题,基于深度确定性策略梯度算法(deep deterministic policy gradient,DDPG),提出一种新的多目标车辆跟随决策算法.根据跟随车辆与领航车辆的相互纵向运动学特性,建立车辆跟随过程的马尔可夫决策过程(Markov decision process,MDP)模型.结合最小安全距离模型,设计一个高效、舒适、安全的车辆跟随决策算法.为提高模型收敛速度,改进了DDPG算法经验样本的存储方式和抽取策略,根据经验样本重要性的不同,对样本进行分类存储和抽取.针对跟车过程的多目标结构,对奖赏函数进行模块化设计.最后,在仿真环境下进行测试,当测试环境和训练环境不同时,依然能顺利完成跟随任务,且性能优于已有跟随算法.