Abstract: There is an urgent need to improve the autonomy, safety, survivability and availability of such critical assets as unmanned systems that are subjected to internal and/or external threats in the execution of a mission. Design for autonomy is taking central stage in the operational needs process development and implementation by responding to significant and urgent safety situations. The industrial and commercial sectors are faced with such needs and challenges. We propose an intelligent strategy for the design of autonomous systems that builds upon concepts from Prognostics and Health Management (PHM) and Fault Tolerant Control (FTC) or reconfigurable control.
The “game changing” aspects of the proposed framework for improved autonomy and its constituent modules are summarized below:
- A rigorous methodology for on-line Remaining Useful Life (RUL) estimation of ailing components will be applied to health management for critical systems with performance guarantees.
- A decision-making module that assesses the integrity of the ailing components and enacts the proper mitigation methodology based on current mission objectives.
- A novel prognostics-based control methodology that utilizes Model Predictive Control and an optimization scheme to trade off system performance for increased RUL, in an attempt to extend the useful life of a degrading asset until its mission is completed.
- Performance and effectiveness metrics to support the optimum design and validation of design for autonomy algorithms.
- A rigorous treatment of trust, risk, confidence and uncertainty management to resolve possible human-automation conflicts and assist the decision making process.
- The integrated integrity management architecture may be implemented on-platform and run in real time. Generic aspects of the approach will be readily applied to other air systems.
We will discuss in this presentation the design for autonomy framework with emphasis on system requirements to monitor their own performance, detect and predict the evolution of fault modes and reconfigure the available control authority in order to safeguard the system integrity in the execution of a mission.

Brief Biography of the Speaker:
Dr. George Vachtsevanos is Professor Emeritus at the Georgia Institute of Technology Dr. Vachtsevanos directs the Intelligent Control Systems laboratory at Georgia Tech where faculty and students conduct research in intelligent control of complex manufacturing, industrial and aerospace systems, reliability and safety of large-scale systems/processes and unmanned systems. Faculty and students in the laboratory began research in diagnostics in 1985 with a series of projects in collaboration with Boeing Aerospace Company funded by NASA and aimed at the development of fuzzy logic based algorithms for fault diagnosis and control of major space station subsystems. Dr. Vachtsevanos and his research team were involved in a series of programs since 1985 in diagnostics and more recently in prognostics funded by government and industry. His research has been supported over the years by ONR, NSWC, the MURI Integrated Diagnostics program at Georgia Tech, the U.S. Army’s Advanced Diagnostic program, General Dynamics, General Motors Corporation, the Academic Consortium for Aging Aircraft program, the U.S. Air Force Space Command, Bell Helicopter, Fairchild Controls, among others. The innovative technologies have relied on both data-driven and model-based algorithms from the domains of soft computing, Dempster-Shafer theory, Bayesian estimation techniques and physics-based modeling architectures. He has been developing innovative diagnostic and prognostic technologies for NASA, ONR, DARPA, and other government agencies. The application domains range from automotive electrical storage and distribution systems, to high power amplifiers, environmental control systems, and critical engine and drive system aircraft components. Of special note are two programs in prognosis of critical aircraft components sponsored by DARPA, the first one in collaboration with Northrop Grumman and other participants and the second with Pratt and Whitney. Dr. Vachtsevanos has developed and has been administering an intensive four-day short course on “Fault Diagnostics/Prognostics for Equipment Reliability and Health Maintenance.”
The group’s research activities in intelligent control and CBM/PHM have been recognized by the community for establishing the foundation for these emerging technologies. More recently, Dr. Vachtsevanos has been investigating the coupling between control and CBM/PHM technologies. Under sponsorship by government and industry his research group is developing innovative fault-tolerant control methodologies aimed to utilize prognostic information and improve the reliability and safety of critical systems.
He has published over 300 technical papers and is the recipient of the 2002-2003 Georgia Tech School of ECE Distinguished Professor Award and the 2003-2004 Georgia Institute of Technology Outstanding Interdisciplinary Activities Award. He is the lead author of a book on Intelligent Fault Diagnosis and Prognosis for Engineering Systems published by Wiley in 2006.