科研内容 Research

我组科研重点强调控制、优化理论和智能网联系统应用的有机结合。主要理论成果包括随机非线性流体网络的分析和控制、非传统排队网络的分析及控制。主要方法是模型预测控制和强化学习算法的结合、动态系统安全博弈分析等,在世界范围内具有开创性、前沿性。主要应用领域包括混合自主性条件下的队列行驶、无信号灯路口的最优/容错控制、信息物理扰动下的交通网络控制、智能网联运输系统的安全控制等。
We are interested in control and optimization problems in smart and connected systems. We focus on the theory of nonlinear flow network control, non-conventional queuing network control. We synthesize model-predictive and learning-based methods to design control algorithms that are tolerant of random faults and secure against strategic attacks. Specific applications include vehicle platooning in mixed autonomy, resilient control of high-speed signal-free intersections, traffic network control under cyber-physical disruptions, and secure control of smart and connected transportation systems.

我组长期和以下高校合作 We collaborate with researchers from the following institutes:

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我组科研活动(正在或曾经)受以下单位资助 Our research is or was sponsored by:

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智能网联汽车队列行驶宏观机理及自适应控制方法研究
Adaptive control for connected and autonomous vehicle platooning

智能网联汽车(CAV)的队列行驶技术有提高公路通行能力、减少交通阻塞、降低单位油耗/排放、改善长途运输业工作环境等诸多效益。国内外在此方面的研究此前主要关注微观层面的协同式自适应巡航控制(CACC)方法,但是队列行驶只有在较大范围内(城际路网)成规模应用才能达到可观的经济、社会、环境效益,而目前还缺乏对宏观层面的演化机理和控制方法研究。我们首先提出了原创的混合排队模型,解决了传统车流模型无法描述路基系统和CAV队列间点对点互动机制的问题,同时解决了已有CAV运动学模型难以推导系统宏观性能(通行效率、等待时间)的问题。基于此模型,我们综合运用经典排队论、马式过程稳定性理论、单调系统控制理论,定量分析了CAV队列对系统性能的影响,给出了混行情境下理论最优控制策略,并在复杂仿真平台得到了验证。我们还将该模型混合状态变量的思想推广到交通流的偏微分方程模型,得到了细化的控制算法。上述成果有助于我国加快智能网联汽车规模化应用的进程,巩固我国在智慧城市、智能交通等领域的领先地位,配合我国“碳达峰碳中和”总体战略。
Connected and autonomous vehicles (CAVs) have a strong potential for improving highway capacities, releasing congestion, reducing fuel consumption and emission, and improving working condition for drivers. We propose an original hybrid queuing model for the control of CAV platoons in mixed autonomy. This model reconciles the lack of scalability and tractability of microscopic, trajectory-based models and the lack of point-to-point tracking and intervention of macroscopic, flow-based models. We use ideas from queuing theory and Markov decision processes to design platooning operations that are adaptive to non-CAV traffic flows as well as environmental non-stationarities.

快速无信号灯路口控制
Control of high-speed signal-free intersections

随着车车/车路协同技术日趋成熟,高速无信号灯路口开始受到关注。这一控制方式具有效率高、灵活性强等特点,具有提升城市道路通行能力的巨大潜能。然而,目前这一领域的研究还存在信息层设计方法和物理层分析方法脱节、微观单车控制理论和宏观系统控制理论脱节等问题。针对这一问题,我们提出适合该场景的分段确定性排队模型,结合马式决策理论和强化学习方法,解决单路口最优控制、多路口协同控制等问题。与此同时,我们还关注通信扰动(时延、丢包)等因素对系统安全和效率的影响,设计切实可行的容错控制方法。
Connectivity and automation of vehicles lead to the idea of high-speed, signal-free intersections. We provide tools and methods to integrate existing results from the microscopic, cyber side with those from the macroscopic, physical side. In particular, we quantitatively relate key communication performance metrics (e.g. latency and packet drop rate) to key transportation performance metrics (e.g. throughput and delay). We apply resilient control methods to design vehicle coordination algorithms tolerant to communication faults. We are also validating the results on small-scale testbeds.

信息物理扰动下的交通网络控制
Traffic network control under cyber-physical disruptions

现代道路运输系统日趋信息化、智能化,因此运输网络的动态控制理论越发受到关注。传统运输网络控制理论主要考虑确定性环境,较少涉及各类随机环境扰动(事故、天气等),因此和各类运输系统实际当中的高随机性有一定脱节。此外,现代运输网络依赖各类信息的实时采集和传输,但目前缺乏分析信息层各类扰动(丢包、时延、故障)对物理层性能影响的理论手段。针对这一问题,我们研究运输网络在各类信息物理扰动下的演化机理,并构建系统性设计抗扰动控制算法的理论工具。我们首先提出原创的分段确定性网络模型来描述运输系统面临的各类信息物理扰动,基于非线性控制和福斯特-李雅普诺夫稳定性理论,分析了运输网络在各类控制方法下的信息物理扰动抗性。在公路运输具体情境下,我们首先提出用马式链模型来描述通行能力的浮动并给出了模型标定算法,并提出了抗扰动匝道控制方法。上述成果可直接应用于城市车流的动态自适应调度,尤其是在各类突发事件/事故下的应急响应,提升城市交通控制系统的可靠性和稳定性,并对其他类型运输系统有直接借鉴意义。
Modern traffic networks are subject to non-traditional cyber-physical disruptions such as observation and communication errors. We study the behavior of feedback-controlled traffic networks in the face of such disruptions. We use the theory of piecewise-deterministic Markov processes and stability of Markov processes to evaluate the impact (e.g. throughput loss) due to such disruptions. We synthesize model-predictive and learning-based methods to design control algorithms that guarantee a certain level of performance even under disruptions. Specific applications include highway incident management and robust ramp metering.

智能网联运输系统的安全控制
Secure control of smart and connected transportation systems

基于实时通信和自主决策的智能网联运输系统在带来各项性能提升的同时,其信息物理安全性也开始受到关注。然而,此前已有研究成果主要关注信息系统的加密保护等内容,但信息和物理层之间的联系研究尚不充分,尤其是较少关注信息攻防双方博弈和物理系统动力学这两项机制的互动联系。针对这一问题,我们利用其随机过程、运筹优化、博弈论等相关背景,研究了各类恶意攻击(主要包括阻断服务攻击和欺骗攻击)对系统效率和安全的影响。我们结合安全博弈模型和动态排队模型建立,运用马式过程理论建立了信息层安全指标(恶意攻击频率、有害信息比例等)和物理层性能指标(事故率、等待时间、油耗等)之间的解析关系。以此为基础,我们正在研究数据诊断、安全响应、架构优化等一系列机制对运输系统的保护作用。我们提出了基于卷积神经网络估计起讫交通流算法,可用于交通数据实时诊断;定量分析了恶意信息对网络控制算法的误导作用,并正在研究应对策略。上述成果可应用于智能网联运输等关键基础设施的信息物理安全风险评估和防护机制设计,对我国公共安全、国家安全都有现实意义。
Connectivity and autonomy bring both efficiency gain as well as cyber security risk to transportation networks. We study the impact of a variety of malicious attacks (mainly denial-of-service and spoofing) on typical transportation systems. We use ideas from stochastic processes, optimization, and game theory to quantitatively relate cyber vulnerabilities to physical risks. Then, we design diagnosis and secure control strategies. We also design protection mechanisms such as dynamically activated security mode for information flows in smart and connected transportation systems.