基于多模型自适应方法的智能汽车路径跟踪控制

路径跟踪控制是智能汽车的一项核心技术,跟踪效果的精确性和在各种路面附着条件下的鲁棒性是该技术的两大关键要素。但汽车动力学模型的不确定性,尤其是轮胎侧偏刚度的摄动使这两者难以同时得到满足。针对这一问题,将多模型自适应理论引入到智能汽车运动控制中处理不确定性系统的控制。首先,推导了多模型自适应控制律,提出了凸包构架下各个顶点的子模型对真实模型的自适应逼近律,并通过李雅普诺夫函数证明了所提出自适应律的收敛能力。在此基础上建立了汽车动力学模型和车辆-路径联合模型,并由多个顶点子模型构建可覆盖汽车轮胎侧偏刚度摄动范围的凸多面体,利用汽车动力学模型求解自适应率,通过车辆-路径联合模型,基于线性二次型方法(linear quadratic regulator,LQR)求解各个顶点的子模型处的反馈控制律,并通过所得出的自适应权重进行加权。基于Carsim/Simulink的联合仿真结果表明,所提出的多模型自适应路径跟踪控制器在保证鲁棒性的同时克服了传统鲁棒控制方法的保守性问题,与基于名义模型的LQR控制器和鲁棒保性能控制器相比,在高附着路面和低附着路面上都可以取得更好的控制效果,很好地解决了路径跟踪控制中精确性与鲁棒性之间的两难问题。最后,通过快速原型测试平台对算法进行了进一步的实验验证。结果表明,所提出的多模型自适应算法实时性良好,具有较好的工程应用潜力。

Reinforcement Learning-Based Energy Management for Hybrid Power Systems:State-of-the-Art Survey,Review,and Perspectives

The new energy vehicle plays a crucial role in green transportation,and the energy management strategy of hybrid power systems is essential for ensuring energy-efficient driving.This paper presents a state-of-the-art survey and review of reinforcement learning-based energy management strategies for hybrid power systems.Additionally,it envisions the outlook for autonomous intelligent hybrid electric vehicles,with reinforcement learning as the foundational technology.First of all,to provide a macro view of historical development,the brief history of deep learning,reinforcement learning,and deep reinforcement learning is presented in the form of a timeline.Then,the comprehensive survey and review are conducted by collecting papers from mainstream academic databases.Enumerating most of the contributions based on three main directions—algorithm innovation,powertrain innovation,and environment innovation—provides an objective review of the research status.Finally,to advance the application of reinforcement learning in autonomous intelligent hybrid electric vehicles,future research plans positioned as“Alpha HEV”are envisioned,integrating Autopilot and energy-saving control.

电控液压全可变气门驱动系统的设计与分析

为了满足发动机设计及性能指标要求,比较分析国内外先进气门执行机构的优缺点,设计一种新型电控液压全可变气门驱动系统.在此基础上,建立气门驱动系统的数学、物理模型,借助MATLAB/Simulink计算平台搭建本系统计算仿真模型并用试验结果进行验证,保证了计算模型的可靠性.根据系统结构,详细分析了可控性参数旋转阀相位差角及蓄压器压力和发动机转速对气门最大升程、气门开启持续期、气门启闭时刻、气门速度及加速度的影响.研究结果表明,旋转阀相位差角通过改变气门开启持续期改变气门关闭时刻,但不影响气门开启段升程规律;蓄压器压力对气门最大升程有重要影响,但不改变气门开启持续期及启闭时刻;在不同发动机转速下,气门最大升程、关闭时刻均有改变;随着发动机转速的提高,气门升程断面积减小,气门关闭时刻推迟.

基于后轮主动脉冲转向的车辆稳定性分析与试验

为了提高车辆操纵稳定性,提出一种后轮主动脉冲转向控制策略,并对此做了理论分析和试验研究。基于试验Lexus车辆分析脉冲转向系统对车辆稳定性能的影响并确定最优的主动转向脉冲参数。设计了控制策略结构与算法,基于Car Sim和Simulink联合仿真分析,验证所提控制方法的有效性。基于试验Lexus车辆,安装液压脉冲转向系统并进行整车试验研究,验证后轮脉冲转向的实用性。仿真和试验结果表明:质心侧偏角和侧向加速度在峰值处分别减小了46.8%、23.5%,提高了汽车的横向稳定性;侧倾因子能控制在设定的阈值范围[-0.8,0.8],车辆侧倾角减小了25.4%,能有效改善车辆防侧翻能力,且展现出比后轮主动转向更好的控制效果。

功能密度梯度泡沫铝填充薄壁锥形结构的吸能性(英文)

为研究功能密度梯度泡沫铝填充薄壁锥形结构在汽车吸能盒上应用的可行性,对锥形泡沫铝、铝合金薄壁结构、泡沫铝填充薄壁结构,进行了单轴准静态压缩试验及数值仿真分析。取得了对功能密度梯度泡沫铝的密度变化梯度和泡沫铝填充薄壁结构的接触面的数值建模的实用性经验。对比试样的变形模式、载荷—位移曲线、吸能量(AE)和比吸能量(SAE)。结果表明,在准静态压缩工况下,截面形状对锥形泡沫铝试样和泡沫铝填充薄壁结构试样的吸能性能影响不大;锥形泡沫铝填充薄壁结构的吸能性能优于对泡沫铝和铝合金薄壁结构的叠加吸能。

电控液压可变气门控制系统的能量回收系统(英文)

电控液压可变气门控制（VVA）系统能够确保气门正时与升程可变,但是系统却消耗大量能量。为了提升它的节能性,本论文提出了一种带有能量回收系统（ERS）的VVA系统。该系统利用一个蓄能器来储存液压缸卸油浪费的能量,蓄能器积累的油压能确保更低主泵能耗。通过在GT-power模型中优化气门正时与升程,来确保带有VVA系统的发动机的燃油消耗率（BSFC）达到最低。结果表明：增加能量回收系统,使气门正时和升程柔性可调,并减少VVA的能量消耗,发动机动力性提升7%~11%,燃油经济性提升6%~9%。

基于滑模技术的单杆柔性机械臂的调节控制

提出一种运用滑模控制技术设计单连杆柔性机械臂鲁棒控制器的方法。滑模控制应用于柔性臂的刚性运动和弹性振动的控制;而极点配置技术的应用,主要是设置滑模面的极点,以获得良好的动态响应特性。实例仿真结果表明,该控制方案能有效地抑制柔性臂的弹性振动,对不确定参数具有鲁棒性。

电控液压可变气门驱动系统鲁棒控制器的设计（英文）

液压油粘度的变化会对液压可变气门系统(VVA)性能产生重要影响,为提高内燃机的性能,应根据发动机的运行工况调整最佳的气门正时及升程。该文建立了全可变气门机构的AMESim仿真模型,分析了液压油粘度对气门升程的影响。基于能量守恒定律建立了系统平均模型,利用Matlab/Simulink设计了滑模控制器。仿真结果表明:所设计的滑模控制器通过调节液压泵的转速,调节气门升程,使气门工作精度提高了9.3%左右。因此,该控制器降低了液压油粘度变化带来的扰动影响。

Study on Rollover Index and Stability for a Triaxle Bus

Vehicle rollover, and its resulting fatalities, is an actively researched topic especially for multi-axle vehicles in the field of vehicle dynamics and control. This paper first presents a new rollover index for a triaxle bus to accurately evaluate its rollover possibility and then discusses the influence laws of the vehicle rollover dynamics to explore the mechanism of its stability. First, a six degree of freedom rollover model of the triaxle bus is developed, including lateral, yaw, roll motion of the sprung mass of the front/rear axle, and roll motion of the unsprung mass of the front/rear axle. Next, some key parameters of the vehicle rollover model are identified. A new rollover index is deduced according to the basics of vehicle dynamics, to predict vehicle rollover risk for the triaxle bus, which is verified by TruckSim. Furthermore, the influence laws of vehicle rollover dynamics by vehicle parameters and road parameters are discussed based on the simulation results. More importantly, the results show that the new method of modeling can precisely describe the rollover dynamics of the studied bus, and the proposed new index can e ectively evaluate the rollover possibility. Therefore, this study provides a theoretical basis to improve anti-rollover ability for triaxle buses.

Probabilistic Lane-Change Decision-Making and Planning for Autonomous Heavy Vehicles

To improve the safety and driving stability of the autonomous heavy truck, it is necessary to consider the differences of driving behavior and drivable trajectories between the heavy trucks and passenger cars. This study proposes a probabilistic decision-making and trajectory planning framework for the autonomous heavy trucks. Firstly, the driving decision process is divided into intention generation and feasibility evaluations, which are realized using the utility theory and risk assessment, respectively. Subsequently the driving decision is made and sent to the trajectory planning module. In order to reflect the greater risks of the truck to other surrounding vehicles, the aggressiveness index(AI) is proposed and quantified to infer the asymmetrical risk level of lane-change maneuver. In the planning stage, the lateral and roll dynamics stability domains are developed as the constraints to exclude the candidate trajectories that would cause vehicle instability. Finally, the simulation results are compared between the proposed model and the artificial potential filed model in the scenarios extracted from the naturalistic driving data. It is shown that the proposed framework can provide the human-like lane-change decisions and truck-friendly trajectories, and performs well in dynamic driving environments.

Proximity Based Automatic Data Annotation for Autonomous Driving

The recent development in autonomous driving involves high-level computer vision and detailed road scene understanding.Today,most autonomous vehicles employ expensive high quality sensor-set such as light detection and ranging(LIDAR)and HD maps with high level annotations.In this paper,we propose a scalable and affordable data collection and annotation framework image-to-map annotation proximity(I2MAP),for affordance learning in autonomous driving applications.We provide a new driving dataset using our proposed framework for driving scene affordance learning by calibrating the data samples with available tags from online database such as open street map(OSM).Our benchmark consists of 40000 images with more than40 affordance labels under various day time and weather even with very challenging heavy snow.We implemented sample advanced driver-assistance systems(ADAS)functions by training our data with neural networks(NN)and cross-validate the results on benchmarks like KITTI and BDD100K,which indicate the effectiveness of our framework and training models.

Design of a Regenerative Auxiliary Power System for Service Vehicles

This paper presents a RAPS,namely the regenerative auxiliary power system,for the vehicle with special accessory power systems.Taking city buses and delivery trucks as examples,service vehicles keep engines idling to power their auxiliary devices(e.g.,refrigeration systems and hydraulic pumps).The potential fuel savings brought on by the electrification of these auxiliary systems are first quantitatively analyzed over a typical drive cycle for a delivery truck.The RAPS is then designed,and its components are sized in accordance with the objectives of compactness and cost-effectiveness.By introducing the proposed RAPS into a conventional delivery truck with an internal combustion engine,the powertrain can be treated as a hybrid because of adding an extra battery.As a result,to pursue a high overall efficiency,a holistic controller is presented for determining how and when to recharge the battery while minimizing the auxiliary system’s power consumption.More importantly,the proposed RAPS saves about 7%fuel when compared with consumption by conventional service vehicles.