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.

Lab Information

PI: Hever Moncayo Ph.D.

For more information, email Dr. Moncayo.

 

Photos

Graphic of small flying taxis
NASA graphic

Research

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.

Faculty

Hever Moncayo

Hever Moncayo, PI

Professor of Aerospace Engineering

Embry-Riddle Aeronautical University

Merve Dogan

Merve Dogan

Assistant Professor of Aerospace Engineering

Embry-Riddle Aeronautical University

Victor Fraticelli

Victor Fraticelli

Assistant Professor in Aeronautical Science

Embry-Riddle Aeronautical University

Maj Mirmirami

Maj Mirmirani

Professor Emeritus of Mechanical Engineering

Embry-Riddle Aeronautical University

Kyriakos Vamvoudakis

Kyriakos Vamvoudakis

Dutton-Ducoffe Endowed Professor of Aerospace Engineering
Georgia Institute of Technology

Petros Ioannou

Petros Ioannou

A.V. "Bal" Balakrishnan Chair

Professor of Electrical and Computer Engineering, 

Aerospace and Mechanical Engineering, and Industrial and Systems Engineering

University of Southern California

Nicholas Gans

Nicholas Gans

Associate Professor Computer Science and Engineering

University of Texas at Arlington

Yijing-Xie

Yijing Xie

Assistant Professor Electrical Engineering

University of Texas at Arlington

Nirmit Prabhakar

Nirmit Prabhakar

Aerospace Engineer of Vehicle Mobility and Simulation

Argone National Laboratory

Kevin Kronfeld

Kevin Kronfeld

Technical Fellow

Collins Aerospace

Advisory Board

Fred Hedaegh

Dr. Fred Hedaegh
Research Professor in Aerospace Engineering
California Institute of Technology

Naira Hovakimyan

Naira Hovakimyan

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

University of Illinois Urbana-Champaign

Trung Pham

Dr. Trung Pham 

Federal Aviation Administration

Chief Scientific and Technical Advisor for AI - Machine Learning

David Doman

Dr. David Doman

Principal Aerospace Engineer

Air Force Research Laboratory

David Klyde

Dr. David Klyde 

Tech Fellow – Flight Controls

Virgin Galactic 

 
James Paunicka

Dr. James Paunicka 

Technical Fellow and Senior Researcher

The Boeing Company