Credit: NASA

Project Description

This NASA University Leadership Initiative (ULI) project, SALA^4, focuses on advancing the safety and resilience of next-generation Advanced Air Mobility (AAM) systems through the development of intelligent autonomy and adaptive control technologies. The research aims to enable real-time detection, self-assessment and response to abnormal conditions, allowing autonomous systems to adjust their behavior dynamically while maintaining safety under operational constraints. These capabilities will support intelligent decision-making and resilient control in complex aviation environments, ensuring safe and reliable operations even in the presence of unexpected events or disturbances.

The project brings together the combined expertise of four academic institutions, a leading industry partner and a national research laboratory. This multidisciplinary collaboration bridges academia and industry to accelerate innovation in aviation safety and autonomy, and to support the integration of autonomous safety architectures into commercial and regulatory frameworks.

Beyond technical advancements, this initiative places strong emphasis on education and workforce development. It will provide research opportunities and career mentoring for both undergraduate and graduate students, as well as support the development of new course materials to cultivate the next generation of aerospace professionals.

Embry-Riddle Faculty

Hever Moncayo
Hever Moncayo

Professor

  • Aerospace Engineering Department
  • College of Engineering
Kadriye Merve Dogan
Kadriye Merve Dogan

Assistant Professor

  • Aerospace Engineering Department
  • College of Engineering
Victor Miguel Fraticelli Rivera
Victor Miguel Fraticelli Rivera

Assistant Professor

  • Aeronautical Science Department
  • College of Aviation

Other Faculty

Maj Mirmirani professional headshot
Maj Mirmirani
Professor Emeritus of Mechanical Engineering

Embry‑Riddle Aeronautical University
Kyriakos Vamvoudakis holding a robotics project
Kyriakos Vamvoudakis
Dutton-Ducoffe Endowed Professor of Aerospace Engineering

Georgia Institute of Technology
Petros Ioannou professional headshot
Petros Ioannou
A.V. "Bal" Balakrishnan Chair
Professor of ECE, AE, ME, ISE 

University of Southern California
Nicholas Gans professional headshot
Nicholas Gans
Associate Professor Computer Science and Engineering

University of Texas at Arlington
Yijing Xie professional headshot
Yijing Xie
Assistant Professor Electrical Engineering

University of Texas at Arlington
Nirmit Prabhakar professional headshot
Nirmit Prabhakar
Aerospace Engineer of Vehicle Mobility and Simulation

Argonne National Laboratory
Kevin Kronfeld headshot
Kevin Kronfeld
Technical Fellow

Collins Aerospace

Advisory Board

Fred Hedaegh professional headshot
Dr. Fred Hadaegh
Research Professor in Aerospace Engineering

California Institute of Technology
Naira Hovakimyan professional headshot

Dr. Naira Hovakimyan
W. Grafton and Lillian B. Wilkins Professor of Mechanical Science and Engineering

University of Illinois Urbana-Champaign

Trung Pham professional headshot
Dr. Trung Pham
Chief Scientific and Technical Advisor for AI - Machine Learning

Federal Aviation Administration
David Doman professional headshot
Dr. David Doman
Principal Aerospace Engineer

Air Force Research Laboratory
David Klyde professional headshot
Dr. David Klyde 
Tech Fellow – Flight Controls

Virgin Galactic
James Paunicka professional headshot
Dr. James Paunicka
Technical Fellow and Senior Researcher

The Boeing Company
Future AAM vehicles are expected to incorporate non-traditional components with high degree of intelligence to maintain the vehicle’s operational safety and performance while dealing with unexpected events and emergencies. The development of a comprehensive self-diagnosis and prognosis approach with cognitive properties will play a vital role in AAM operations as it will increase safety assurance by maintaining mission requirements under operational constraints. The general goal of this ULI project is to overcome long-standing limitations in current knowledge that have limited and/or delayed the integration of AAM systems into the NAS. We are developing a safety assurance multi-functional system with self-learning attributes, that enables reliable supervision, management and control of AAM vehicles. The system generates safety-related specifications that are then integrated to generate optimized safe trajectories and bounded learning control actions under "learned” operational constraints. In addition, we are developing cognitive performance metrics to constantly monitor the efficiency of the intelligent components within the safety assurance architecture.
Existing autonomy frameworks are primarily built around fixed self-properties, such as self-configuration, self-optimization and self-awareness, defined by preset mission goals and execution strategies. However, these approaches lack the flexibility to adapt to evolving environments and current assurance methods assume systems remain unchanged after deployment, limiting their ability to learn or evolve. To address this gap, there is a growing need for a dynamic and adaptive safety assurance architecture that can support learning-enabled systems, enhance real-time decision-making and ensure safe, reliable integration of AAM vehicles into the NAS.
Current trajectory planning and control frameworks are designed primarily to compensate for uncertainties and disturbances without regard to the system’s status, current state or operational envelope. This limitation could be critical, especially in scenarios where precise actions under unforeseen system degradation must be guaranteed. As a result, the trajectory or control actions that are effective under normal conditions may not fully align with the required safety constraints under new sets of operational conditions. The integration of advanced onboard trajectory planning and intelligent control reconfiguration within a safety assurance architecture will be beneficial for ensuring that precise and safe adjustments are made with a full understanding of the vehicle’s capabilities and limitations.
Future AAM vehicles will likely be operated fully or partially autonomously and will be equipped with learning elements to enhance operational capabilities. Measuring how a system with cognitive properties executes actions, makes decisions and learns by extracting the appropriate information from a complex collection of data, is a necessary step in certification and represents a significant design challenge. Addressing this challenge requires developing appropriate tools and metrics to monitor the effectiveness of these systems to evolve, scale and generalize under a variety of scenarios. Linking learning with operational safety by continuously assessing the impacts of the system’s actions as they evolve, will be essential, not only to enhance system performance and reliability but also to build trust in their deployment in sensitive and dynamic environments.
  • March 6, 2026:  Speaker: Eugene Lavretsky
  • January 30, 2026:  Speaker: Melanie Zeilinger
  • December 1, 2025:  Speaker: Jay Wilhelm
  • October 29, 2025:  Speaker: Kerianne ‘Yoda’ Hobbs
  • September 24, 2025:  Speaker: Petros Ioannou

NASA SALA4 Project Team gathered for the official kickoff meeting

Students work on an autonomous vehicle.
Internships available across our research lab partners.
STEM activities for K-12 students.
Graduate and undergraduate students involvement in experiential learning.
Woman wrights out a formula on a marker board.
Workshops in leadership and workforce development.
Young students play on a simulator during STEM Outreach
Girls in Aviation 2025
Students and teacher participate in STEM day activities
Girls in Aviation 2025
Young students participate in simulator demonstration
Girls in Aviation 2025
Students listen to a lecture during STEM day
Teach-In Day at Sally Ride Elementary
Students work on a computer during STEM Day
Teach-In Day at Sally Ride Elementary
Students play with controllers and a simulator during a STEM outreach
Teach-In Day at Sally Ride Elementary
Students play a game with plastic cups during STEM outreach
Teach-In Day at Sally Ride Elementary

SALA^4 NASA ULI Repository

NASA ULI project advancing safety and resilience of Advanced Air Mobility with intelligent autonomy and adaptive control technologies.

Related Resources

Contact Us

Hever Moncayo, Ph.D.
Principal Investigator