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.

现代联网车辆系统正在利用互联技术的进步来推动全球主要市场的创新。例如，智能交通将车辆和基础设施连接起来，可显着提高燃油效率（高达 22%）和乘客安全（冬季事故减少 25%），是世界上增长最快的行业之一。然而，在这些复杂的互连系统中，用于数据融合、估计和控制的传统集中式架构（计算效率）极其低下。集中式系统的灵活性和模块化程度有限，使得网络容易出现故障和干扰，单点故障就可能危及整个系统。基于子控制数据系统的新型网络控制系统通过通信网络交换信息并利用分布式机器学习增强算法，为这些挑战提供了一种有前景的解决方案。 为日益流行的网络物理系统、自动驾驶系统（ADS）和协作车辆提供快速和可重构机制的潜力，凸显了开发更可靠的分布式系统设计的迫切需要。由于此类网络系统日益复杂，当前基于模型的分布式控制方法正在达到其性能极限，从而影响模型的决策预测能力以及系统对意外事件和通信干扰的恢复能力。因此，拟议的研究计划将利用描述每个子系统主要属性的实验数据，为网络车辆系统推进一种新的学习辅助分布式估计和控制平台，这些数据通过这些网络节点的宽带通信（具有高数据速率）提供。 总体长期目标是为网络车辆系统开发一种新的控制和诊断范例，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，该团队将通过结合基于模型和学习的结构，利用 5G 等新型无线接入技术提供的更低延迟和更高数据速率，解决核心挑战，使分布式系统在计算上更加高效和可靠。 NR。将追求两个综合目标：1）网络系统中的学习辅助分布式估计； 2）分布式学习控制算法的开发。 其结果将是为连接的 ADS 和智能交通设计一个可扩展且有弹性的分布式框架，而无需子系统节点知道确切的全局系统模型，从而在分布式系统的学习和控制能力方面提供潜在的突破。该团队还将为加拿大智能交通行业培养下一代创新者。