**Abstract** In modern fighter aircraft with statically unstable airframe designs, the flight control system is considered flight critical, i.e. the aircraft will encounter a catastrophic event if the system fails. Consequently, the system design has to meet high levels of reliability and failure immunity. In this presentation, an overview will be provided on the basic concepts employed in flight critical system design for fault tolerance. Different methods of redundancy implementation, failure management, and health management will be discussed. Basic concepts in fault diagnostics will also be discussed, along with some new concepts for implementation of prognostics in reducing levels of physical system redundancy. **Bio** David S. Bodden is Lockheed Martin Senior Fellow. He recieved BS in Aerospace Engineering from Texas A&M University in 1976 and MS in Engineering Science and Mechanics from Virginia Tech in 1984. Mr. Bodden’s technical career has encompassed a broad scope of technology areas. He has worked in structural dynamics, advanced design, conceptual design, flight control systems, and prognostics. His management experience includes six years as Chief of the Control Law design and Analysis Group followed by seven years as the Senior Manager of Flight Control Systems. Mr. Bodden was selected as a Technical Fellow in Flight Controls at LM Aeronautics in 2002 and as a Senior Fellow in 2007. Mr. Bodden has authored numerous papers and technical proposals, and managed numerous technology development programs. He served on the AIAA Guidance, Navigation, and Control Technical Committee, he has served as the Chairman of the Lockheed Martin Corporate Task Force on Guidance and Control, he initiated and served as Chairman of the Lockheed Martin GNC Technology Focus Group, is former Chairman of the SAE Aerospace Control and Guidance Systems Committee, and he currently serves as Chairman of the Texas A&M Aerospace Advisory Board.

This paper demonstrates a novel optimizationbased approach to estimating fault states in a DC power system. The model includes faults changing the circuit topology along with sensor faults. Our approach can be considered as a relaxation of the mixed estimation problem. We develop a linear model of the circuit and pose a convex problem for estimating the faults and other hidden states. A sparse fault vector solution is computed by using l1 regularization. The solution is computed reliably and efficiently, and gives accurate diagnostics on the faults. We demonstrate a real-time implementation of the approach for an instrumented electrical power system testbed at NASA. Accurate estimates of multiple faults are computed in milliseconds on a PC. The approach performs well despite unmodeled transients and other modeling uncertainties present in the system.

Diagnosis and prognosis are necessary tasks for system reconfiguration and fault-adaptive control in complex systems. Diagnosis consists of detec- tion, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents an integrated model-based distributed diagnosis and prognosis framework, where system decomposi- tion is used to perform the diagnosis and prog- nosis tasks in a distributed way. We show how different submodels can be automatically con- structed to solve the local diagnosis and prog- nosis problems. We illustrate our approach us- ing a simulated four-wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs fault diagnosis and prognosis in a robust manner.

Fault diagnosis is key to ensuring system safety through fault-adaptive control. This task is diffcult in hybrid systems with combined continuous and discrete behaviors because mode changes make diagnosability hard to achieve. Including additional sensors can improve diagnosability, but that is not always feasible. An alternative strategy is active diagnosis, where we improve the diagnosis result by executing or blocking controllable events. We present a qualitative, event-based approach to active diagnosis of hybrid systems, where we automatically synthesize event-based diagnosers for hybrid systems that can determine if the system is diagnosable through passive or active diagnosis. We apply our active diagnosis scheme to a real-world electrical power distribution system.

Actuator systems are employed widely in aerospace, transportation and industrial processes to provide power to critical loads, such as aircraft control surfaces. They must operate reliably and accurately in order for the vehicle / process to complete successfully its designated mission. Incipient actuator failure conditions may severely endanger the operational integrity of the vehicle / process and compromise its mission. The ability to maintain a stable and credible operation, even in the presence of incipient failures, is of paramount importance to accomplish “must achieve” mission objectives. This paper introduces a novel methodology for the fault-tolerant design of critical subsystems, such as an ElectroMechanical Actuator (EMA), that takes advantage of on-line, real-time estimates of the Remaining Useful Life (RUL) or Time-to-Failure (TTF) of a failing component and reconfigures the available control authority by trading off system performance with control activity. The primary goal is to complete critical mission objectives within a time window dictated by prognostic algorithms so that the fault mode is accommodated and an acceptable level of performance maintained for the duration of the mission. The proposed fault-tolerant control design is mathematically rigorous, generic and applicable to a variety of application domains. An EMA is used to illustrate the efficacy of the proposed approach.

Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions. **Reference:** B. W. Ricks and O. J. Mengshoel, "Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study." In Proc. of the First Annual Conference of the Prognostics and Health Management Society (PHM-09), San Diego, CA, September 27 – October 1, 2009. **BibTex Reference:** @inproceedings{ricks09methods, author = {Ricks, B. W. and Mengshoel, O. J.}, title = {Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study}, booktitle = {Proc. of the Annual Conference of the Prognostics and Health Management Society (PHM-09)}, address = {San Diego, CA}, month = sep, year = {2009} }

This control methodology utilizes real-time vehicle structural load and shape measurements to actively respond to and protect against vehicle damage due to structural overload. The innovation utilizes critical point load feedback within an optimal control allocation architecture that constrains the load at those critical points while still producing the control response commanded by a pilot. Specifically, the technology monitors the loads at critical control points and shifts the loading away from points at or near their limits.

Work to date: Using NASA’s Full-Scale Advanced Systems Testbed (FAST) aircraft, the Armstrong team targeted the aileron hinge connection as a critical control point. The experiment produced successful results in simulation and flight, preventing structural overload and yielding good handling characteristics for optimization metrics and load constraint types.

Looking ahead: This effort led to being awarded an ARMD seedling fund phase one to further develop the technique and expand the work to other autonomy efforts. Future tests will employ more advanced and unique sensor technologies, such as fiber optic strain sensors. This technology could open the door to truly novel approaches to vehicle and control system design.

Benefits

• Effective: Identifies the optimum control surface usage for a given maneuver for both performance and structural loading
• Automated: Monitors and alleviates stress on critical load points in real time
• Economical: Decreases the need for repairs and general maintenance

Applications

• Jet aircraft
• Rocket controls
• Industrial robotics
• Structural health monitoring and load alleviation

Failure prognosis - as a natural extension to the fault detection and isolation (FDI) problem - has become a key issue in a world where the economic impact of system reliability and cost-effective operation of critical assets is steadily increasing. Failure prognostic algorithms aim to characterize the evolution of incipient fault conditions in complex dynamic processes, thus allowing to estimate of the remaining useful life (RUL) of subsystems and components. Several examples can be used here to illustrate the range of possible applications for these algorithms: electro-mechanical systems, continuous-time manufacturing processes, structural damage analysis, and even fault tolerant software architectures. Most of them have in common the fact that they are highly complex, nonlinear, and affected by large-grain uncertainty. We introduce in this chapter an integrated failure prognosis architecture that is applicable to a variety of aircraft systems and industrial processes. We are targeting a specific rotorcraft system as a prototypical testbed for proof-of-concept. The overall architecture consists of an on-board and an off-board module for eventual on-platformimplementation purposes.

Diagnosis and prognosis are necessary tasks for system re- configuration and fault-adaptive control in complex systems. Diagnosis consists of detection, isolation and identification of faults, while prognosis consists of prediction of the remain- ing useful life of systems. This paper presents a novel inte- grated framework for model-based distributed diagnosis and prognosis, where system decomposition is used to enable the diagnosis and prognosis tasks to be performed in a distributed way. We show how different submodels can be automati- cally constructed to solve the local diagnosis and prognosis problems. We illustrate our approach using a simulated four- wheeled rover for different fault scenarios. Our experiments show that our approach correctly performs distributed fault diagnosis and prognosis in an efficient and robust manner.

This paper introduces a novel Prognostics-enhanced Automated Contingency Management (or ACM+P) paradigm based on both current health state (diagnosis) and future health state estimates (prognosis) for advanced autonomous systems. Including prognostics in ACM system allows not only fault accommodation, but also fault mitigation via proper control actions based on short term prognosis, and moreover, the establishment of a long term operational plan that optimizes the utility of the entire system based on long term prognostics. Technical challenges are identified and addressed by a hierarchical ACM+P architecture that allows fault accommodation and mitigation at various levels in the system ranging from component level control reconfiguration, system level control reconfiguration, to high level mission re-planning and resource redistribution. The ACM+P paradigm was developed and evaluated in a high fidelity Unmanned Aerial Vehicle (UAV) simulation environment with flight-proven baseline flight controller and simulated diagnostics and prognostics of flight control actuators. Simulation results are presented. The ACM+P concept, architecture and the generic methodologies presented in this paper are applicable to many advanced autonomous systems such as deep space probes, unmanned autonomous vehicles, and military and commercial aircraft.