多工况以及多目标优化的自适应巡航系统

为提高自适应巡航系统(Adaptive Cruise Control,ACC)的综合性能,通过对跟驰性能、安全性、燃油经济性以及乘客舒适性进行分析,作为系统的控制约束,并引入了基于驾驶数据的车头时距.采用了分层控制的架构,并基于模型预测控制理论(Model Predictive Control,MPC)设计了上层控制器...     

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。     

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...     

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。     

汽车自适应巡航系统激光雷达扫描平面定位

基于扫描式激光雷达的基本原理和自适应巡航控制(ACC)系统的工作要求,指出了对激光雷达扫描平面进行定位的目的和意义。通过对2个点目标和楔形块斜面进行扫描测量,确定了激光雷达扫描透镜即激光探头在光学保护罩内的深度和激光扫描平面的高度,并进行了补充实验对测量结果加以验证,证明测量误差在1 cm以内,为确定激光雷达在汽车上的理想安装位置,减小系统误差,避免ACC系统发生误报打下了基础。     

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。     

联合通信资源分配的网联车协同自适应巡航 时滞反馈控制

考虑网联车辆队列在路段通信资源受限下的协同自适应巡航控制(CACC)问题,提出一种联合通信资源分配的网联车辆协同自适应巡航时滞反馈控制方法.首先,在头车-前车跟随的通信拓扑结构下,通过网联车辆队列中各车辆间的通信链路数量、该路段可使用的通信资源和当前时刻车辆间的间距误差建立二分图,根据车辆间的间距误差来调度有限的通信资源,将通信资源合理分配给有较大间距误差的跟随车辆;其次,利用非对称PD控制协议和网联车辆队列时滞纵向模型,应用线性矩阵不等式技术计算网联车CACC控制器,进一步得到车辆队列弦稳定性的充分条件;最后,通过Matlab/CarSim联合仿真验证该方法的有效性.     

协同自适应巡航控制车队仿真

如何评估道路环境等外部条件变化对协同自适应巡航控制（Cooperative Adaptive Cruise Control,CACC）车队行驶安全性的影响,对于保障道路交通安全尤为重要。为此,结合Matlab/Simulink和CarSim搭建车辆-环境仿真平台研究道路环境对车队行驶安全性的影响。利用美国NGSIM车辆轨迹数据对平台采用的校正的预瞄驾驶员模型、加速度控制模型、节气门控制模型和制动器控制模型的控制器进行验证;利用平台开展红灯状态、隧道行驶和匝道行驶三种道路环境对车队行驶状态影响的仿真实验。仿真结果表明：车队可以顺利通过路口红绿灯;在安全车速范围内车队进出隧道口时方向控制良好;匝道坡度主要影响车辆加速度和车间距,坡度分别为3%和5%时,车队均保持稳定车间距安全行驶。     

汽车自适应巡航控制跟随模式的仿真建模

自适应巡航控制跟随模式的研究是一个非常复杂的问题,大量试验分析及理论推导表明,传统的寻优控制方法难以获得满意的结果。该文介绍了自适应巡航控制及跟随模式的功能,根据模糊神经网络控制理论,详细分析了自适应巡航控制跟随模式下的距离控制,构造了适宜的五层模糊神经网络,对汽车自适应巡航控制跟随模式进行了仿真建模,推导了相应BP算法公式,并利用相关资料在Matlab环境下进行了仿真试验。分析结果表明,使用模糊神经网络技术能够取得满意的控制效果。     

面向自适应巡航控制的数字孪生仿真系统研究

针对传统仿真方法在测试内容、效率和成本等方面的挑战,提出面向自适应巡航控制的数字孪生仿真系统（Adaptive Cruise Control Twin Simulation System,ACC-TwinSim）。采用CarSim和MATLAB的Simulink工具,构建了自适应巡航控制的虚拟仿真场景,同时利用数字孪生技术实时模拟和监控物理系统,通过rtamt库验证自适应巡航控制仿真系统的行为规范,提高了测试的效率和实验的可靠性。     

Vehicle adaptive cruise control design with optimal switching between throttle and brake

For vehicle adaptive cruise control(ACC) systems,the switching performance between throttle and brake determines the driving comfort,fuel consumption and service lives of vehicle mechanical components.In this paper,an ACC algorithm with the optimal switching control between throttle and brake is designed in model predictive control (MPC) framework.By introducing the binary integer variables,the dynamics of throttle and brake are integrated in one model expression for the controller design.Then the ACC algorithm is designed to satisfy not only safe car following,but also the optimal switching between throttle and brake,which leads to an online mixed integer quadratic programming solved by the nested two-loop method.The simulation results show that the proposed ACC algorithm meets the requirements of safe car following,outperforms the traditional algorithms by performing smoother responses,reducing the switching times between throttle and brake,and therefore improves driving comfort and fuel efficiency significantly.     

Data-iteration cooperative adaptive cruise control of mixed heterogeneous vehicle platoons with unknown dynamic characteristics

This paper considers the cooperative adaptive cruise control (CACC) problem of mixed heterogeneous vehicle platoons composed of human-driven and CACC vehicles with unknown dynamic characteristics and an alternative to CACC for platooning of the heterogeneous vehicle platoon is presented. Adaptive dynamic programming is firstly used to learn the dynamic characteristics of acceleration of vehicles from the sampled data. Then the data-iteration optimal CACC controller is computed to ensure that each CACC vehicle can reach a desired inter-vehicle distance and desired common velocity with no prior knowledge of the dynamics of vehicles in the mixed platoon. Moreover, the string stability of the mixed vehicle platoon is derived by establishing some sufficient conditions on the acceleration transfer function of adjacent vehicles. Two simulation experiments of a six-vehicle mixed platoon are used to illustrate the effectiveness of the proposed CACC method.     

Real-time optimization of energy consumption under adaptive cruise control for connected HEVs

This paper presents a real-time energy optimization algorithm for a hybrid electric vehicle (HEV) that operates with adaptive cruise control (ACC). Real-time energy optimization is an essential issue such that the HEV powertrain system is as efficient as possible. With connected vehicle technique, ACC system shows considerable potential of high energy efficiency. Combining a classical ACC algorithm, a two-level cooperative control scheme is constructed to realize real-time power distribution for the host HEV that operates in a vehicle platoon. The proposed control strategy actually provides a solution for an optimal control problem with multi objectives in terms of string stable of vehicle platoon and energy consumption minimization of the individual following vehicle. The string stability and the real-time optimization performance of the cooperative control system are confirmed by simulations with respect to several operating scenarios.     

通信中断时的网联车辆协作自适应巡航控制

针对自主巡航车队发生通信中断的情况,提出一种由协作式自适应巡航控制(CACC)模式切换至自适应巡航控制(ACC)模式的方法.当通信中断时,将车辆按照通信能力分组优化处理,并将通信中断车辆由CACC模式切换到ACC模式,且保持原CACC模式下的时间间距不变;为了保证车队安全,结合避碰理论和拉格朗日中值定理,提出一种基于最小安全距离的时间间距切换策略;为了保证两次切换后混合巡航车队(车队中既有CACC车辆也有ACC车辆)的稳定性,结合终值定理和频域分析法设计了混合巡航模式下的CACC-ACC控制器.最后通过仿真与现有方法对比,验证了所提出方法的有效性.     

Robust predictive cruise control for commercial vehicles

Abstract

In this paper we explore learning-based predictive cruise control and the impact of this technology on increasing fuel efficiency for commercial trucks. Traditional cruise control is wasteful when maintaining a constant velocity over rolling hills. Predictive cruise control (PCC) is able to look ahead at future road conditions and solve for a cost-effective course of action. Model- based controllers have been implemented in this field but cannot accommodate many complexities of a dynamic environment which includes changing road and vehicle conditions. In this work, we focus on incorporating a learner into an already successful model- based predictive cruise controller in order to improve its performance. We explore back propagating neural networks to predict future errors then take actions to prevent said errors from occurring. The results show that this approach improves the model based PCC by up to 60% under certain conditions. In addition, we explore the benefits of classifier ensembles to further improve the gains due to intelligent cruise control.     

Discrete-time minimal control synthesis adaptive algorithm

This article proposes a discrete-time Minimal Control Synthesis (MCS) algorithm for a class of single-input single-output discrete-time systems written in controllable canonical form. As it happens with the continuous-time MCS strategy, the algorithm arises from the family of hyperstability-based discrete-time model reference adaptive controllers introduced in (Landau, Y. (1979), Adaptive Control: The Model Reference Approach, New York: Marcel Dekker, Inc.) and is able to ensure tracking of the states of a given reference model with minimal knowledge about the plant. The control design shows robustness to parameter uncertainties, slow parameter variation and matched disturbances. Furthermore, it is proved that the proposed discrete-time MCS algorithm can be used to control discretised continuous-time plants with the same performance features. Contrary to previous discrete-time implementations of the continuous-time MCS algorithm, here a formal proof of asymptotic stability is given for generic n-dimensional plants in controllable canonical form. The theoretical approach is validated by means of simulation results.     

非线性系统的改进型自适应积分控制算法

针对传统非线性系统的积分控制,为减小稳态跟踪误差和防止积分饱和,设计了一种改进型的自适应积分控制。采用反馈线性化消去系统的非线性,在初始误差较大的条件下,引入惩罚函数根据误差动态调节比例系数和积分系数可抑制较大的超调量。应用Lyapunov稳定性理论,证明了在外部扰动有界的条件下,闭环控制系统一致最终有界。闭环系统稳定性的理论分析和仿真结果都验证了上述策略的可行性。     

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。