Learning-Aided Distributed Estimation and Control for Networked Vehicular Systems

网络车辆系统的学习辅助分布式估计和控制

• 批准号: RGPIN-2020-05097
• 负责人: Hashemi, Ehsan
• 金额: $ 1.68万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=716671
• 关键词: Learning; Aided; Distributed; Estimation; Control

Modern networked vehicular systems are leveraging advances in connectivity to drive innovation in major markets across the globe. Intelligent transportation, for example, which connects vehicles and infrastructure to enable dramatic improvements in fuel efficiency (up to 22%) and passenger safety (25% fewer accidents in winter conditions), is one of the world's fastest growing industries. However, traditional centralized architectures for data fusion, estimation, and control in these complex, interconnected systems are prohibitively inefficient (computationally). Centralized systems have limited flexibility and modularity, rendering the network susceptible to faults and disturbances that could imperil the entire system from just a single point of failure. New networked control systems built upon sub-control data systems that exchange information through a communication network and that leverage distributed, machine learning-enhanced algorithms present a promising solution to these challenges. The potential to enable fast and reconfigurable mechanisms for increasingly prevalent cyber-physical systems, Automated Driving Systems (ADS), and cooperative vehicles, underscores the critical need to develop more reliable distributed-system designs. Current model-based distributed control approaches are reaching their performance limits due to the growing complexity of such networked systems, therefore impacting the model's predictive capacity for decision-making and the resilience of the system to unexpected events and communication disturbances. Therefore, the proposed research program will advance a new learning-aided distributed estimation and control platform for networked vehicular systems using experimental data that describes the main properties of each subsystem, provided through broadband communication (with high data rates) across these networked nodes. The overarching, long-term goal is to develop a new control and diagnosis paradigm for networked vehicular systems, enabling increased reliability and performance through co-design of control and learning algorithms. Through the next five years, the team will address core challenges to rendering distributed systems more computationally efficient and reliable by combining model- and learning-based structures, taking advantage of the lower latency and higher data rates provided by new radio access technologies such as 5G NR. Two integrated objectives will be pursued: 1) Learning-aided distributed estimation in networked systems; and 2) Development of distributed learning control algorithms. The result will be design of a scalable and resilient distributed framework for connected ADS and intelligent transportation without requiring the exact global system model to be known to the subsystem nodesoffering potential breakthroughs in the distributed system's learning and control capacity. The team will also train the next generation of innovators for Canada's intelligent transportation industry.

现代的网络车辆系统利用了连通性的进步，以推动全球主要市场的创新。例如，智能运输将车辆和基础设施连接起来，以使燃油效率的显着提高（高达22％）和乘客安全性（在冬季情况下少25％）是世界上增长最快的行业之一。但是，在这些复杂的，相互联系的系统中，用于数据融合，估计和控制的传统集中式体系结构极低效率（计算上）。集中式系统具有有限的灵活性和模块化，使网络容易受到故障和干扰的影响，这些故障和干扰可能仅仅是单个故障。新的网络控制系统建立在子控制数据系统上，该系统通过通信网络交换信息，并利用分布式，机器学习增强的算法为这些挑战提供了有希望的解决方案。 实现越来越普遍的网络物理系统，自动驾驶系统（AD）和合作车辆的快速和重新配置机制的潜力，强调了开发更可靠的分布式系统设计的关键需求。由于此类网络系统的复杂性日益增长，因此当前基于模型的分布式控制方法正在达到其性能限制，因此影响了模型的决策能力以及系统对意外事件和通信干扰的弹性。因此，拟议的研究计划将使用实验数据来推进网络车辆系统的新的学习辅助分布式估计和控制平台，该实验数据描述了通过宽带通信（具有高数据速率）在这些网络节点中提供的每个子系统的主要属性。 总体，长期的目标是为网络车辆系统开发新的控制和诊断范式，从而通过控制和学习算法的共同设计来提高可靠性和性能。在接下来的五年中，团队将通过结合模型和基于学习的结构来解决分布式系统的核心挑战，从而利用较低的潜伏期和较高的数据速率（例如5G NR）提供的较低的延迟和较高的数据速率。将实现两个综合目标：1）网络系统中学习辅助分布式估计； 2）开发分布式学习控制算法。 结果将是针对连接的广告和智能运输的可扩展和弹性的分布式框架的设计，而无需要求确切的全局系统模型是子系统节点，从而为分布式系统的学习和控制能力中的潜在突破而知道。该团队还将为加拿大智能运输行业培训下一代创新者。