Hybrid Systems and Control

信息物理系统（CPS）是由基于计算机的算法控制的物理工程系统。这样的系统正越来越多地渗透到我们的生活中：执行搜索和救援任务的无人驾驶飞行器（uav），在繁忙的交通中导航的自动驾驶汽车，以及执行救生手术的手术机器人——这些只是将先进的控制算法与物理系统集成在一起的几个例子，它们可以带来重要的应用。许多网络物理系统，包括上述激励应用程序，对安全至关重要：它们的故障可能导致灾难性后果。例如，欧洲航天局花了10年时间和70亿美元制造的阿丽亚娜5号火箭，由于数字软件设计中的一个小错误，在不到1分钟的时间内坠毁。最近特斯拉（Tesla）自动驾驶仪（AutoPilot）的故障导致了多起致命事故，这是另一个警告信号，表明我们仍然对信任自动机器缺乏信心。机器学习算法的日益普及也提出了一个根本性的重要问题，即人工智能是否信任对安全至关重要的CPS的控制。事实上，如何以经济的方式构建和有效地认证CPS安全、可靠和值得信赖，仍然是学术界和工业界面临的一个基本挑战。在这些挑战的激励下，本研究旨在推进安全关键网络物理系统（CPS）的最先进的数学控制理论和设计方法。长期目标是系统地开发经过验证的计算方法，用于CPS的稳健设计，并将其转化为实践。未来五年的短期目标是：(1)探索有效计算的理论极限和控制CPS的正式方法；(2)从严格的规范出发，设计高效的鲁棒控制综合算法；(3)利用控制理论、学习和优化的协同作用来实现CPS的安全关键控制。

Cyber-physical systems (CPS) are physical engineering systems controlled by computer-based algorithms. Such systems are increasingly penetrating our lives: unmanned air vehicles (UAVs) executing search and rescue missions, autonomous cars navigating through busy traffic, and surgical robots performing life-saving operations---these are just a few examples of how integrations of advanced control algorithms with physical systems can lead to important applications. Many cyber-physical systems, including the above motivating applications, are safety-critical: their failure can lead to catastrophic consequences. For example, the Ariane 5 rocket, which took the European Space Agency 10 years and $7 billions to produce, crashed in less than 1 minute due to a small error in the numerical software design. The recent failures of Tesla's AutoPilot that caused multiple fatal accidents is another warning sign that we still significantly lack confidence in trusting automated machines. The increasingly pervasive use of machine learning algorithms also raises the fundamentally important question of AI trust in control of safety-critical CPS. Indeed, how to affordably build and efficiently certify CPS as safe, reliable, and trustworthy remains a fundamental challenge in both academia and industry. Motivated by these challenges, this research aims to advance the-state-of-the-art of mathematical control theory and design methods for safety-critical cyber-physical systems (CPS). The long-term goal is to systematically develop validated computational methods for robust design of CPS and translate them into practice. The short-term objectives over the next five years are to: (O1) explore the theoretical limits of validated computation and formal methods for control of CPS; (O2) design efficient algorithms for robust control synthesis for CPS from rigorous specifications; (O3) exploit the synergies of control theory, learning, and optimization for safety-critical control of CPS.