Observation from NASA Research on AAM / UAM eVTOL Aircraft

Biosketch

Wayne Johnson obtained SB, SM, and Doctor of Science degrees from the Massachusetts Institute of Technology. He worked at the U.S. Army Aeromechanics Laboratory from 1970 to 1981, assigned to the 40- by 80-Foot Wind Tunnel branch of Ames Research Center, and with NASA from 1981 to 1986. In 1986, Dr. Johnson founded Johnson Aeronautics, and from 1986 to 1998 developed rotorcraft software. Since 1998 he has worked at the Aeromechanics Branch of NASA Ames Research Center. Dr. Johnson is author of the comprehensive analysis CAMRADII and the rotorcraft design code NDARC; and the books “Helicopter Theory” (1980) and “Rotorcraft Aeromechanics” (2013). He is a Fellow of AIAA and AHS, and an Ames Fellow, and has received the U.S. Army Commander’s Award for Civilian Service, NASA Medals for Exceptional Engineering Achievement and Exceptional Technology Achievement, the AHS Grover E. Bell Award, the Ames H. Julian Allen Award, the AIAA Pendray Aerospace Literature Award, the 2010 AHS Alexander Nikolsky Honorary Lectureship, the 2014 Alexander Klemin Award of the American Helicopter Society, and the 2023 Daniel Guggenheim Medal.

Abstract

The seminar will describe the aeronautics research of the NASA Revolutionary Vertical Lift Technology Project, which is currently focused on design and analysis tools and test data to support the development and operations of Advanced Air Mobility and Urban Air Mobility eVTOL aircraft. AAM missions are characterized by ranges below 300 nm, including rural and urban operations, passenger carrying as well as cargo delivery. Urban Air Mobility (UAM) is a subset of AAM and is the segment that is projected to have the most economic benefit and be the most difficult to develop. With an emphasis on community acceptance (noise and annoyance) and electric propulsion, aircraft for the AAM/UAM missions introduce new challenges that require innovative solutions to the usual tasks of rotorcraft development. The NASA RVLT project is developing UAM VTOL aircraft designs that can be used to focus and guide research activities in support of aircraft development for emerging aviation markets. These NASA concept vehicles encompass relevant UAM features and technologies, including propulsion architectures, highly efficient yet quiet rotors, and aircraft aerodynamic performance and interactions. Already these UAM concept aircraft have been used in numerous engineering investigations, including work on meeting safety requirements, achieving good handling qualities, and reducing noise below helicopter certification levels. Focusing on the concept vehicles, observations can be made regarding the engineering of Advanced Air Mobility aircraft.

Coordinated Learning-based Autonomy for Urban Air Mobility Operations

Biosketch

Dr. Peng Wei is an associate professor in the Department of Mechanical and Aerospace Engineering at the George Washington University, with courtesy appointments at
The emerging electric vertical take-off and landing (eVTOL) aircraft technologies and urban air mobility (UAM) concepts are expected to change the aviation landscape. In order to enable safe and high-density UAM operations, new mathematical models and autonomy algorithms are needed to support flight planning, air traffic management, and avionics functions. In this talk, the speaker will initiate exciting and open-ended discussions on the new possible flight planning and coordination models, learning-based separation assurance algorithms, and AI certification concerns in aviation autonomy. Electrical and Computer Engineering Department and Computer Science Department. By contributing to the intersection of control, optimization, machine learning, and artificial intelligence, he develops autonomy and certification tools for aviation, aeronautics, and aerial robotics. His current focus is on the safety, efficiency, and scalability of decision-making systems in complex, uncertain, and dynamic environments. Prof. Wei is an AIAA Associate Fellow. He serves as an associate editor for the AIAA Journal of Aerospace Information Systems, and the chairperson of the AIAA Air Transportation Systems Technical Committee. He received his Ph.D. degree in Aerospace Engineering from Purdue University in 2013 and his bachelor’s degree in Control Theory from Tsinghua University in 2007.

Abstract

The emerging electric vertical take-off and landing (eVTOL) aircraft technologies and urban air mobility (UAM) concepts are expected to change the aviation landscape. In order to enable safe and high-density UAM operations, new mathematical models and autonomy algorithms are needed to support flight planning, air traffic management, and avionics functions. In this talk the speaker will initiate exciting and open-ended discussions on the new possible flight planning and coordination models, learning-based separation assurance algorithm, and AI certification concerns in aviation autonomy.

Safe Learning and Control with L1 Adaptation

Biosketch

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 the 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 450 refereed publications. She is the 2011 recipient of the AIAA Mechanics and Control of Flight Award, the 2015 recipient of the SWE Achievement Award, the 2017 recipient of the IEEE CSS Award for Technical Excellence in Aerospace Controls, and the 2019 recipient of the AIAA Pendray Aerospace Literature Award. In 2014 she was awarded the Humboldt prize for her lifetime achievements. She is a Fellow of the AIAA and IEEE and a senior member of the National Academy of Inventors. She is the co-founder and chief scientist of Intelinair. Her work was featured in the New York Times, on Fox TV, and on CNBC.

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. In the first part of the talk, we present an overview of L₁ adaptive control, how it enables safety in autonomous robots, and discuss some of its success stories in the aerospace industry. In the second part of the talk, we present some of our recent results that explore various architectures with L₁ adaptive control while guaranteeing performance and robustness throughout the learning process. An overview of different projects at our lab that build upon this framework will be demonstrated to show different applications.