Hybrid Systems and Control

计算机物理系统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外科机器人-这些只是先进控制算法与物理系统的集成如何产生重要应用的几个例子。许多网络物理系统，包括上述激励应用程序，都是安全关键型的：它们的故障可能会导致灾难性的后果。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，由于数值软件设计中的一个小错误，在不到1分钟的时间里就坠毁了。最近，特斯拉的自动驾驶仪出现故障，导致多起致命事故，这是另一个警告信号，表明我们仍然严重缺乏信任自动化机器的信心。机器学习算法的日益普遍使用也提出了一个根本重要的问题，即人工智能信任对安全关键的CP的控制。事实上，如何以负担得起的价格建立并有效地证明CPS是安全、可靠和值得信赖的，仍然是学术界和工业界面临的一个根本挑战。在这些挑战的推动下，本研究旨在推进安全关键的网络物理系统(CPS)的数学控制理论和设计方法的最新发展。长期目标是系统地为CPS的稳健设计开发经过验证的计算方法，并将它们转化为实践。未来五年的短期目标是：(O1)探索CPS控制的有效计算和形式方法的理论极限；(O2)根据严格的规范设计用于CPS的稳健控制综合的高效算法；(03)为CPS的安全关键控制开发控制理论、学习和优化的协同作用。

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.