czzc1990的博客

The platooning of autonomous vehicles has the potential to significantly improve traffic capacity, enhance highway safety, and reduce fuel consumption. This paper studies the scalability limitations of large-scale vehicular platoons moving in rigid formation, and proposes two basic ways to improve stability margins, i.e., enlarging information topology and employing asymmetric control. A vehicular platoon is considered as a combination of four components: 1) node dynamics; 2) decentralized controller; 3) information flow topology; and 4) formation geometry. Tools, such as the algebraic graph theory and matrix factorization technique, are employed to model and analyze scalability limitations. The major findings include: 1) under linear identical decentralized controllers, the stability thresholds of control gains are explicitly established for platoons under undirected topologies. It is proved that the stability margins decay to zero as the platoon size increases unless there is a large number of following vehicles pinned to the leader and 2) the stability margins of vehicular platoons under bidirectional topologies using asymmetric controllers are always bounded away from zero and independent of the platoon size. Simulations with a platoon of passenger cars are used to demonstrate the findings.

The platooning of connected and automated vehicles has the potential to significantly benefit the road traffic, including enhancing highway safety, improving traffic capacity, and reducing fuel consumption. This paper presents a four-component analysis framework for platoon systems from a networked control perspective, including a literature review by network awareness, unified models of key components, and two application cases for controller synthesis. The networked control perspective naturally decomposes a platoon into four interrelated components, namely, 1) node dynamics (ND), 2) information flow topology (IFT), 3) formation geometry (FG), and 4) distributed controller (DC). The existing literature is categorized under this framework and analyzed according to the component features. The unified mathematical models are derived for platoons with linear dynamics and distributed controllers. As a case study, a distributed controller synthesis method is introduced for homogeneous platoons, which guarantees the internal stability in the presence of a broad class of topologies with/without uniform time-delays. The effectiveness of the proposed method is demonstrated using numerical simulations

The platooning of connected and automated vehicles (CAVs) is expected to have a transformative impact on road transportation, e.g., enhancing highway safety, improving traffic utility, and reducing fuel consumption. Requiring only local information, distributed control schemes are scalable approaches to the coordination of multiple CAVs without using centralized communication and computation. From the perspective of multi-agent consensus control, this paper introduces a decomposition framework to model, analyze, and design the platoon system. In this framework, a platoon is naturally decomposed into four interrelated components, i.e., 1) node dynamics, 2) information flow network, 3) distributed controller, and 4) geometry formation. The classic model of each component is summarized according to the results of the literature survey; four main performance metrics, i.e., internal stability, stability margin, string stability, and coherence behavior, are discussed in the same fashion. Also, the basis of typical distributed control techniques is presented, including linear consensus control, distributed robust control, distributed sliding mode control, and distributed model predictive control

In a platoon control system, a fixed and symmetrical topology is quite rare, because of adverse communication environments and continuously moving vehicles. This paper presents a DASMC (Distributed Adaptive Sliding Mode Control) scheme for more realistic vehicular platooning. In this scheme, adaptive mechanism is adopted to handle platoon parametric uncertainties, while a structural decomposition coupling of interaction topology. mA enthuomde rdiceaall sa wlgiothri ththme based on LMI (Linear Matrix Inequality) is developed to rpelaqcueir etdh ea rpeoa letos boafl atnhcee sqluidicinkgn emsso taionnd sdmynoaomthincse ssin. Tthhee proposed scheme allows the nodes to interact with each other via different types of topologies, e.g., either aDsifyfmermenett rfircoaml oerx isstyinmgm teetcrhicnailq, ueeisth, eitr dfoixeesd n ootr rsewquiticreh inthge. exact values of each entity in topological matrix, and only enfefeedctsi vetnoe sksn oofw t hitsh ep robpoousnedds m eotfh oidtso loegigye ins vvaalluideast. edT hbey bench tests under several conditions.

The paper presents a curving adaptive cruise control (ACC) system that is coordinated with a direct yawmoment control (DYC) system and gives consideration to both longitudinal car-following capability and lateral stability on curved roads. A model including vehicle longitudinal and lateral dynamics is built first, which is as discrete as the predictive model of the system controller. Then, a cost function is determined to reflect the contradictions between vehicle longitudinal and lateral dynamics. Meanwhile, some I/O constraints are formulated with a driver permissible longitudinal car-following range and the road adhesion condition. After that, desired longitudinal acceleration and desired yaw moment are obtained by a linear matrix inequality based robust constrained state feedback method. Finally, driver-in-the-loop tests on a driving simulator are conducted and the results show that the developed control system provides significant benefits in weakening the impact of DYC on ACC longitudinal car-following capability while also improving lateral stability.

Besides automated controllers, the information flow among vehicles can significantly affect the dynamics of a platoon. This paper studies the influence of information flow topology on the closed-loop stability of homogeneous vehicular platoon moving in a rigid formation. A linearized vehicle longitudinal dynamic model is derived using the exact feedback linearization technique, which accommodates the inertial delay of powertrain dynamics. Directed graphs are adopted to describe different types of allowable information flow interconnecting vehicles, including both radar-based sensors and V2V communications. Under linear feedback controllers, a unified closed-loop stability theorem is proved by using the algebraic graph theory and Routh–Hurwitz stability criterion. The theorem explicitly establishes the stabilization threshold of linear controller gains for platoons with a large class of different information flow topologies. Numerical simulations are used to illustrate the results.

Platoon formation of highway vehicles has the potential to significantly enhance road safety, improve highway utility, and increase traffic efficiency. However, various uncertainties and disturbances that are present in real-world driving conditions make the implementation of vehicular platoon a challenging problem. This study presents an H-infinity control method for a platoon of heterogeneous vehicles with uncertain vehicle dynamics and uniform communication delay. The requirements of string stability, robustness and tracking performance are systematically measured by the Hinfinity norm, and explicitly satisfied by casting into the linear fractional transformation format. A delay-dependent linear matrix inequality is derived to numerically solve the distributed controllers for each vehicle. The performances of the controlled platoon are theoretically analysed by using a delay-dependent Lyapunov function which includes a linear quadratic function of states during the delay period. Simulations with a platoon of heterogeneous vehicles are conducted to demonstrate the effectiveness of the proposed method under random parameters and external disturbances.

This article investigates sampled-data vehicular platoon control with communication delay. A new sampled-data control method is established, in which the effect of the communication delay is involved. First, a linearized vehicle longitudinal dynamic model is obtained using the exact feedback-linearization technique. Then, under the leader–predecessor following communication strategy, considering communication delay, a platoon control law is proposed based on sampled state information, which allows the weights of state errors to vary along the platoon. Complemented by additional string stability conditions, a useful string-stable platoon controller design algorithm is proposed. Finally, the effectiveness of platoon controller design methodology is demonstrated by numerical examples.

Decoupling of linear time-invariant systems by state feedback and precompensation has been treated in several papers (Falb and Wolovich 1967, Gilbert 1969, Wonham and Morse 1970, Silverman and Payne 1(71). A generalization of some results for time-variable systems was given by Porter (1969) and Freund (1971 a, b). Decoupling of non-linear time-variable systems by state feedback was considered by Porter (1970) where a feedback law as a sufficient condition for decoupling was presented. In this paper the results of Porter (1970) on decoupling are extended for the case D(x, t),., 0 and a more general feedback law is derived. This feedback law permits an arbitrary assignment of a specified number of poles and is a generalization of the synthesis procedure of Falb and Wolovich (1967) for the non-linear time-variable case. In the subsequent section the structure of the decoupled closed-loop system with respect to observability is considered. This type of problem has been regarded for linear systems with constant coefficients by Mufti (1969). For this purpose a sufficient criterion for observability of non-linear timevariable systems is introduced and applied to the closed-loop decoupled system with arbitrary pole assignment. The analysis based on this criterion leads to results about the structure of the decoupled system and to a sufficient condition.... for the observability of the decoupled non-linear time-variable system.

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

作为一个十余年来快速发展的崭新领域，深度学习受到了越来越多研究者的关注，它在特征提取和建模上都有着相较于浅层模型显然的优势．深度学习善于从原始输入数据中挖掘越来越抽象的特征表示，而这些表示具有良好的泛化能力．它克服了过去人工智能中被认为难以解决的一些问题．且随着训练数据集数量的显著增长以及芯片处理能力的剧增，它在目标检测和计算机视觉、自然语言处理、语音识别和语义分析等领域成效卓然，因此也促进了人工智能的发展．深度学习是包含多级非线性变换的层级机器学习方法，深层神经网络是目前的主要形式，其神经元间的连接模式受启发于动物视觉皮层组织，而卷积神经网络则是其中一种经典而广泛应用的结构．卷积神经网络的局部连接、权值共享及池化操作等特性使之可以有效地降低网络的复杂度，减少训练参数的数目，使模型对平移、扭曲、缩放具有一定程度的不变性，并具有强鲁棒性和容错能力，且也易于训练和优化．基于这些优越的特性，它在各种信号和信息处理任务中的性能优于标准的全连接神经网络．该文首先概述了卷积神经网络的发展历史，然后分别描述了神经元模型、多层感知器的结构．接着，详细分析了卷积神经网络的结构，包括卷积层、池化层、全连接层，它们发挥着不同的作用．然后，讨论了网中网模型、空间变换网络等改进的卷积神经网络．同时，还分别介绍了卷积神经网络的监督学习、无监督学习训练方法以及一些常用的开源工具．此外，该文以图像分类、人脸识别、音频检索、心电图分类及目标检测等为例，对卷积神经网络的应用作了归纳．卷积神经网络与递归神经 网络的集成是一个途径．为了给读者以尽可能多的借鉴，该文还设计并试验了不同参数及不同深度的卷积神经网络来分析各参数间的相互关系及不同参数设置对结果的影响．最后，给出了卷积神经网络及其应用中待解决的若干问题

提出了一种基于卷积神经网络的前方车辆检测方法。首先，根据车底阴影特征，运用基于边缘增强的路面检测算法以及车底阴影自适应分割算法来分割并形成车底候选区域，以解决路面灰度分布不均及光照条件变化问题；其次，运用针对道路交通环境的卷积神经网络结 构，建立图像样本库进行网络训练；在此基础上，采用基于卷积神经网络识别的方法以验证并剔除被误检测为车底阴影的候选区域，进而确定真正的车辆目标；最后，修改网络为三分类识别，以验证本文方法的强扩展性的优势。实验结果表明：本文提出的车辆检测方法能够很好地区分车底阴影和非车底阴影干扰，有效地提高车辆检测的准确率和可靠性，降低误检率。

针对现有的基于卷积神经网络的图像超分辨率算法参数较多、计算量较大、训练时间较长、图像纹理模糊等 问题，结合现有的图像分类网络模型和视觉识别算法对其提出了改进。在原有的三层卷积神经网络中，调整卷积 核大小，减少参数；加入池化层，降低维度，减少计算复杂度；提高学习率和输入子块的尺寸，减少训练消耗的时间； 扩大图像训练库，使训练库提供的特征更加广泛和全面。实验结果表明，改进算法生成的网络模型取得了更佳的 超分辨率结果，主观视觉效果和客观评价指标明显改善，图像清晰度和边缘锐度明显提高。

针对近年来滑模变结构控制的发展状况, 将滑模变结构控制分为18个研究方向, 即滑模控制的消除抖振问题、准滑动模态控制、基于趋近律的滑模控制、离散系统滑模控制、自适应滑模控制、非匹配不确定性系统滑模控 制、时滞系统滑模控制、非线性系统滑模控制、Terminal滑模控制、全鲁棒滑模控制、滑模观测器、神经网络滑模控 制、模糊滑模控制、动态滑模控制、积分滑模控制和随机系统的滑模控制等. 对每个方向的研究状况进行了分析和说明. 最后对滑模控制的未来发展作了几点展望.

针对一类高阶不确定非线性系统, 基于指数型快速终端滑模的良好特性, 提出了一种新的滑模变结构控制.系统状态变量能以较快的收敛速度在有限时间内到达各级滑模面的邻域, 并最终收敛到平衡点附近很小的区域.使用李亚普诺夫稳定性理论证明了系统的渐近稳定性, 并推导出各级邻域和系统不确定环节的数学关系.Matlab仿真验证了系统的强鲁棒性

提出一种用于非匹配不确定MIMO 线性系统的终端滑模控制方法。设计了特殊的终端滑模切 换面和相应的控制策略, 使得系统在有限时间内收敛到终端滑模面, 在系统到达终端滑模面后保证系统 的状态保持在终端滑模面上, 并在有限时间内收敛到平衡点附近的邻域内。建立了该邻域的范围与系统 的非匹配不确定性的范围以及终端滑模参数之间的数学关系, 用于系统设计与分析。仿真结果验证了所 提出方法的有效性和分析的正确性。