AIAA Intelligent Systems Technical Committee

ISTC Technical Seminar Series Don't miss Dr. Melkior Ornik's seminar this Tuesday July 23 at 2:00pm ET on Zoom! Speaker: Melkior Ornik, Ph.D. Department of Aerospace Engineering University of Illinois Urbana-Champaign Date/time: Tuesday, July 23rd, 2024 -- 2:00pm-3:00pm Eastern time Title: Control of Unknown Systems in Unlearnable Environments: Fundamental Limits of Knowledge Meeting link: https://lnkd.in/geF9wdn3 Abstract: High-level autonomy in previously unseen or abruptly changed conditions faces a critical conceptual challenge: if the controller has no comprehensive system model and no prior opportunity to collect data and train its strategy, how can we form guarantees about system safety or performance? In fact, how do we know whether the system's task is even feasible? Indeed, standard approaches of learning-based control require a wealth of available data, while classical methods of robust and adaptive control respond to highly structured uncertainties. Additionally, attempting to determine a control strategy to complete a predetermined task does not reflect the limits of the system's capabilities: the system might be unable to perform the old task in novel conditions. Instead, an intelligent planner should understand which tasks can certifiably be completed given the current knowledge, and then formulate appropriate control laws. To move towards that goal, in this talk I will present an emergent twin efforts of design-time guaranteed resilience and mission-time guaranteed performance. Combining methods of optimal control, reachability analysis, and differential geometry, these approaches compute a set of tasks completable under all system dynamics consistent with the planner’s partial knowledge, and synthesize appropriate control laws using online learning and adaptation. In describing this framework, this talk will briefly present several applications to aerial and space vehicles, identifying promising future directions of research such as safety-assured reachability, verifiable performance with faulty sensing, and data-driven incremental certification. Bio: Melkior Ornik is an assistant professor in the Department of Aerospace Engineering at the University of Illinois Urbana-Champaign, also affiliated with the Coordinated Science Laboratory, as well as the Discovery Partners Institute in Chicago. He received his Ph.D. degree from the University of Toronto in 2017. His research focuses on developing theory and algorithms for control, learning and task planning in autonomous systems that operate in uncertain, changing, or adversarial environments, as well as in scenarios where only limited knowledge of the system is available. He is a senior member of AIAA and IEEE, his recent work has been extensively funded by NASA grants and Department of Defense programs, and he has been awarded the 2023 Air Force Young Investigator Program grant. #aiaaistc

Join us for the 2024 Intelligent Systems Workshop on June 25-26 in Lincoln, Nebraska! See the website for more details, including the agenda: https://lnkd.in/gbkPspib Don't delay, sign up today! :) #aiaaistc

ISTC Technical Seminar Series Don't miss Dr. Jun Chen's seminar tomorrow (this Tuesday) May 14 at 4:00pm ET on Zoom! Speaker: Jun Chen, Ph.D. Department of Aerospace Engineering San Diego State University Date/time: Tuesday, May 14th, 2024 -- 4:00pm-5:00pm Eastern time Title: Safety-Assured Online Planning for Large-scale Autonomy under Uncertainty Meeting link: https://lnkd.in/gsfdEq7D Dial by your location 877 853 5257 US Toll-free 888 475 4499 US Toll-free Meeting ID: 248 132 0441 Passcode: 013418 Abstract: With the booming of artificial intelligence and autonomy in a new era, the field of systems and control has recently been facing newly emerged research in the control and optimization of large-scale networked autonomous systems, most of which heavily rely on the fidelity of the models and efficient computational techniques to execute optimized control actions. Meanwhile, the physical autonomous systems are inherently subject to uncertainties and disturbances. This seminar will present a suite of modeling, optimization, and computation algorithms and tools that can efficiently support safety-assured real-time decision-making for large-scale autonomous systems, with a focus on Unmanned Aerial Vehicle (UAV) autonomy and Advanced Air Mobility (AAM) applications under dynamic and uncertain environments. Bio: Dr. Jun Chen is an Assistant Professor in the Department of Aerospace Engineering at San Diego State University. Dr. Chen's research area includes dynamics, control, machine learning, and artificial intelligence, particularly in data-driven modeling, control, and optimization for large-scale networked dynamical systems, with applications in mechanical and aerospace engineering such as air traffic control, traffic flow management, and autonomous air/ground vehicle systems. His research spans theory and practice, including both algorithm development and real-world field tests. Dr. Chen’s research has been supported by NSF, FAA, and NASA. Dr. Chen earned his Ph.D. and M.S. degrees in Aerospace Engineering from Purdue University and a B.S. degree in Aeronautics Engineering from Beihang University. He is a recipient of the Purdue College of Engineering Outstanding Research Award in 2018. #aiaaistc

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ISTC Technical Seminar Series Don't miss Dr. Jun Chen's seminar Tuesday May 14 at 4:00pm ET on Zoom! Speaker: Jun Chen, Ph.D. Department of Aerospace Engineering San Diego State University Date/time: Tuesday, May 14th, 2024 -- 4:00pm-5:00pm Eastern time Title: Safety-Assured Online Planning for Large-scale Autonomy under Uncertainty Meeting link: https://lnkd.in/gsfdEq7D Dial by your location 877 853 5257 US Toll-free 888 475 4499 US Toll-free Meeting ID: 248 132 0441 Passcode: 013418 Abstract: With the booming of artificial intelligence and autonomy in a new era, the field of systems and control has recently been facing newly emerged research in the control and optimization of large-scale networked autonomous systems, most of which heavily rely on the fidelity of the models and efficient computational techniques to execute optimized control actions. Meanwhile, the physical autonomous systems are inherently subject to uncertainties and disturbances. This seminar will present a suite of modeling, optimization, and computation algorithms and tools that can efficiently support safety-assured real-time decision-making for large-scale autonomous systems, with a focus on Unmanned Aerial Vehicle (UAV) autonomy and Advanced Air Mobility (AAM) applications under dynamic and uncertain environments. Bio: Dr. Jun Chen is an Assistant Professor in the Department of Aerospace Engineering at San Diego State University. Dr. Chen's research area includes dynamics, control, machine learning, and artificial intelligence, particularly in data-driven modeling, control, and optimization for large-scale networked dynamical systems, with applications in mechanical and aerospace engineering such as air traffic control, traffic flow management, and autonomous air/ground vehicle systems. His research spans theory and practice, including both algorithm development and real-world field tests. Dr. Chen’s research has been supported by NSF, FAA, and NASA. Dr. Chen earned his Ph.D. and M.S. degrees in Aerospace Engineering from Purdue University and a B.S. degree in Aeronautics Engineering from Beihang University. He is a recipient of the Purdue College of Engineering Outstanding Research Award in 2018. #aiaaistc

ISTC Technical Seminar Series Don't miss Dr. Naira Hovakimyan's seminar this Tuesday April 23 at 5:00pm ET on Zoom! Speaker: Naira Hovakimyan, Ph.D. W. Grafton and Lillian B. Wilkins Professor Mechanical Science and Engineering University of Illinois Urbana-Champaign Date/time: Tuesday, Apr 23th, 2024 -- 5:00pm-6:00pm Eastern time Title: Safe Learning in Autonomous Systems Meeting link: https://lnkd.in/gsfdEq7D Dial by your location 877 853 5257 US Toll-free 888 475 4499 US Toll-free Meeting ID: 248 132 0441 Passcode: 013418 Abstract: Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external disturbances is going to be vital to ensure mission success. We introduce a class of distributionally robust adaptive control architectures that ensure robustness to distribution shifts and enable the development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications. Bio: Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 500 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award, the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019 recipient of AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. She is Fellow of AIAA, IEEE, ASME, and senior member of National Academy of Inventors. She is cofounder and chief scientist of Intelinair. Her work was featured in the New York Times, on Fox TV and CNBC. #aiaaistc

ISTC Technical Seminar Series Don't miss Dr. Naira Hovakimyan's seminar Tuesday April 23 at 5:00pm ET on Zoom! Speaker: Naira Hovakimyan, Ph.D. W. Grafton and Lillian B. Wilkins Professor Mechanical Science and Engineering University of Illinois Urbana-Champaign Date/time: Tuesday, Apr 23th, 2024 -- 5:00pm-6:00pm Eastern time Title: Safe Learning in Autonomous Systems Meeting link: https://lnkd.in/gsfdEq7D Dial by your location 877 853 5257 US Toll-free 888 475 4499 US Toll-free Meeting ID: 248 132 0441 Passcode: 013418 Abstract: Learning-based control paradigms have seen many success stories with various robots and co-robots in recent years. However, as these robots prepare to enter the real world, operating safely in the presence of imperfect model knowledge and external disturbances is going to be vital to ensure mission success. We introduce a class of distributionally robust adaptive control architectures that ensure robustness to distribution shifts and enable the development of certificates for V&V of learning-enabled systems. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications. Bio: Naira Hovakimyan received her MS degree in Applied Mathematics from Yerevan State University in Armenia. She got her Ph.D. in Physics and Mathematics from the Institute of Applied Mathematics of Russian Academy of Sciences in Moscow. She is currently W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering and the Director of AVIATE Center of UIUC. She has co-authored two books, eleven patents and more than 500 refereed publications. She is the 2011 recipient of AIAA Mechanics and Control of Flight Award, the 2015 recipient of SWE Achievement Award, the 2017 recipient of IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019 recipient of AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. She is Fellow of AIAA, IEEE, ASME, and senior member of National Academy of Inventors. She is cofounder and chief scientist of Intelinair. Her work was featured in the New York Times, on Fox TV and CNBC. #aiaaistc

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