Mary S. Reveley and Jeffrey L. Briggs

Glenn Research Center, Cleveland, Ohio

Karen M. Leone

ASRC Aerospace Corporation, Cleveland, Ohio

Tolga Kurtoglu

Stinger Ghaffarian Technologies, Inc., Mountain View, California

Colleen A. Withrow

Glenn Research Center, Cleveland, Ohio

Assessment of the State of the Art of Flight Control 

Technologies as Applicable to Adverse Conditions

NASA/TM—2010-216226

September 2010
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Mary S. Reveley and Jeffrey L. Briggs

Glenn Research Center, Cleveland, Ohio

Karen M. Leone

ASRC Aerospace Corporation, Cleveland, Ohio

Tolga Kurtoglu

Stinger Ghaffarian Technologies, Inc., Mountain View, California

Colleen A. Withrow

Glenn Research Center, Cleveland, Ohio

Assessment of the State of the Art of Flight Control 

Technologies as Applicable to Adverse Conditions

NASA/TM—2010-216226

September 2010

National Aeronautics and

Space Administration

Glenn Research Center

Cleveland, Ohio 44135
Available from

NASA Center for Aerospace Information

7115 Standard Drive

Hanover, MD 21076–1320

National Technical Information Service

5301 Shawnee Road

Alexandria, VA 22312

Available electronically at http://gltrs.grc.nasa.gov

Trade names and trademarks are used in this report for identifi cation 

only. Their usage does not constitute an offi cial endorsement, 

either expressed or implied, by the National Aeronautics and 

Space Administration.

Level of Review: This material has been technically reviewed by technical management. 
NASA/TM—2010-216226 iii 

Contents 

Summary ....................................................................................................................................................... 1

1.0 Introduction ............................................................................................................................................ 2

2.0 Establishing Adverse Conditions Related to Aviation Safety ................................................................ 2

2.1 Causal Factor Analyses of National Transportation Safety Board, Federal Aviation 

Administration, and Aviation Safety Reporting System Accident and Incident Data ................... 2

2.2 Adverse Conditions Table .............................................................................................................. 3

3.0 General Assessment of State of the Art of Flight Control ...................................................................... 4

3.1 Compensating for Damage and/or Reduced Performance ............................................................. 4

3.2 Retrofit Upgrades ........................................................................................................................... 5

3.3 Flight Control Through Engine Response ...................................................................................... 5

3.4 Test and Validation of New Adaptive Controllers ......................................................................... 5

4.0 Flight Control Systems and Technologies in Relation to Specific Adverse Conditions ........................ 6

4.1 Adverse Conditions Involving Failure of an Aircraft System or Component ................................ 6

4.1.1 Landing Gear..................................................................................................................... 6

4.1.2 Avionics ............................................................................................................................ 7

4.1.3 Electrical ........................................................................................................................... 7

4.1.4 Hydraulics ......................................................................................................................... 8

4.1.5 Static and Dynamic Actuators ........................................................................................... 9

4.1.6 Environmental Control System ......................................................................................... 9

4.2 Adverse Conditions Involving Damage to a Structure or Component ......................................... 10

4.2.1 Propulsion System ........................................................................................................... 10

4.2.2 Control Surfaces .............................................................................................................. 11

4.2.3 Aerodynamic and Structural Damage ............................................................................. 12

4.3 Adverse Conditions Involving Control Upsets Related to Pilot Error or Another Cause That 

Cannot Be Regulated by Aircraft Technology ............................................................................. 13

4.3.1 Electrical ......................................................................................................................... 13

4.3.2 Severe Weather ............................................................................................................... 14

4.3.3 Inadequate Attitude/Airspeed and Stall/Spin .................................................................. 15

4.3.4 Pilot ................................................................................................................................. 15

5.0 Conclusion ............................................................................................................................................ 16

References ................................................................................................................................................... 17

Appendix A.—Acronyms ........................................................................................................................... 23
NASA/TM—2010-216226 1 

Assessment of the State of the Art of Flight Control Technologies 

as Applicable to Adverse Conditions 

Mary S. Reveley and Jeffrey L. Briggs 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Karen M. Leone 

ASRC Aerospace Corporation 

Cleveland, Ohio 44135 

Tolga Kurtoglu 

Stinger Ghaffarian Technologies, Inc. 

Moffett Field, California 94035 

Colleen A. Withrow 

National Aeronautics and Space Administration 

Glenn Research Center 

Cleveland, Ohio 44135 

Summary 

A survey of literature from academia, industry, and other Government agencies was reviewed to 

assess the state of the art in flight control technologies as related to the identified adverse conditions. A 

general state of the art in adaptive flight control is summarized first, followed by an assessment of the 

state of the art as applicable to 13 identified adverse conditions. The state-of-the-art summaries include 

technologies not specifically related to flight control, but may serve as inputs to a future flight control 

algorithm. 

The adverse conditions were determined as a result of a previous study and can be found in NASA 

Technical Memorandum entitled “Causal Factors and Adverse Conditions of Aviation Accidents and 

Incidents Related to Integrated Resilient Aircraft Control.” The types of adverse conditions are divided 

into three separate groups: failure of aircraft system or component, damage to a structure or component, 

and control upsets related to pilot error or another cause that cannot be regulated by aircraft technology.

In general, there were two distinct methods for implementing adaptive flight control systems. In the 

first method, the system assists the pilot in operating the aircraft in a degraded state by providing 

additional information on the degradation state of the aircraft and then makes calculations relating to 

alternative use of controls or alternative maneuvers. The second type of implementation involves no 

change in the pilot’s method of operation in relation to a nominal operating environment. The adaptive 

control system takes the nominal control inputs given by the pilot, performs calculations based on the 

degradation state of the aircraft, and sends alternative commands to the vehicle control surfaces and/or 

propulsion system. This method is very different than the first, as the pilot is not required to alter the 

operation of the plane due to the degradation.

Much more research is being done on failures and degradation of specific components in relation to 

adaptive control systems. Although component research is important, as it can provide better input data 

into adaptive control systems, more can be done to advance the state of the art of the actual adaptive 

control systems. In regards to the two methods of implementing adaptive control, the trend is that the state 

of the art is moving in the direction of systems in which the pilot operates the aircraft the same in both 

nominal and off-nominal conditions where the adaptive control system itself alters the commands that go 

to the control surfaces and propulsion system when operating in an off-nominal state.
NASA/TM—2010-216226 2 

1.0 Introduction 

The study reported here has been completed for the Integrated Resilient Aircraft Control (IRAC) 

Project (Ref. 1), which is a part of the National Aeronautics and Space Administration’s (NASA’s) 

Aviation Safety Program (AvSAFE). Specifically, this study addresses the assessment of the state of the 

art of flight control systems and/or technologies as applicable to adverse conditions. The adverse 

conditions were determined as a result of a previous study and can be found in NASA Technical 

Memorandum entitled “Causal Factors and Adverse Conditions of Aviation Accidents and Incidents 

Related to Integrated Resilient Aircraft Control” (Ref. 2). 

Table 1 summarizes the results of an examination of statistical and prognostic data to interpret and 

extract information about accidents and incidents caused by loss of control. The table includes potential 

adverse conditions against which flight, propulsion, and mission-adaptive control approaches can be 

evaluated. In this analysis, publicly available accident and incident data from the National Transportation 

Safety Board (NTSB), Federal Aviation Administration (FAA), and Aviation Safety Reporting System 

(ASRS) were examined, and 13 categories of adverse condition subtypes are documented. All of these 

data sources can be accessed using the Aviation Safety Information Analysis and Sharing (ASIAS) 

System (Ref. 3).The types of adverse conditions are divided into three separate groups: failure of aircraft 

system or component, damage to a structure or component, and control upsets related to pilot error or 

another cause that cannot be regulated by aircraft technology.

The study reported here is based on research related to flight control systems and technology in 

general, as well as specific information related to each of the 13 subtype categories of adverse conditions. 

Included is a summary of the results of a survey of literature from academia, industry, and other 

Government agencies to assess the state of the art in flight control technologies as related to the identified 

adverse conditions. First, the state of the art in adaptive flight control is summarized. Next, the state of the 

art in flight control technology is discussed in regards to each category of adverse conditions. These 

summaries may include state-of-the-art technologies not specifically related to flight control that may 

serve as inputs to a future flight control algorithm, such as advanced sensor data. These discussions are 

meant to serve as a summary of the technologies. An indepth discussion of each technology mentioned 

would not be feasible in a report of this breadth. The references given provide more detailed information. 

Additional references not cited in the paper that may offer a broader perspective of the current 

technologies are available by contacting the author. 

2.0 Establishing Adverse Conditions Related to Aviation Safety 

NASA conducted an analysis to document the results of an examination of statistical and prognostic 

data to interpret and extract information about the causes of loss-of-control accidents and incidents. In this 

analysis, publicly available accident and incident data from the NTSB, FAA, and ASRS were examined.

2.1 Causal Factor Analyses of National Transportation Safety Board, Federal Aviation 

Administration, and Aviation Safety Reporting System Accident and Incident Data 

The analysis was conducted to determine the causal factors of accidents and incidents associated with 

loss of control of commercial aircraft during 1988 to 2003. In this analysis, “commercial” is defined as 

Part 121, Scheduled Part 135, and Nonscheduled Part 135 flights. Part 121 operations applies to major 

airlines and cargo carriers that fly large transport-category aircraft, while Part 135 applies to commercial 

aircraft air carriers commonly referred to as commuter airlines. Prior to March 1997, Part 121 operations 

included aircraft with 30 or more seats. In March 1997, the definition of Part 121 operations changed and 

now includes those aircraft with 10 or more seats. Scheduled operation refers to “any common carriage 

passenger-carrying operation for compensation or hire conducted by an air carrier or commercial operator 

for which the certificate holder or its representative offers in advance the departure location, departure 

time, and arrival location.” A nonscheduled operation refers to “any operation for compensation or hire in 
NASA/TM—2010-216226 3 

which the departure time, departure location, and arrival location are specifically negotiated with the 

customer” (Ref. 4). The safety risks of both accidents and incidents are defined as follows (Ref. 5): 

Accident an occurrence associated with the operation of an aircraft, which takes place between the 

time any person boards the aircraft with the intention of flight and all such persons have 

disembarked, and in which any person suffers death or serious injury, or in which the 

aircraft receives substantial damage 

Incident an occurrence other than an accident, associated with the operation of an aircraft, which 

affects or could affect the safety of operations 

The source for accident data is the NTSB Aviation Accident and Incident Data System, while the 

source for incident data is the ASRS Incident Data System. 

For each accident and incident, the cause of the loss of control was determined. In some cases, 

multiple causal factors were cited for accidents or incidents. The data is summarized in a table that is 

explained in the next section.

2.2 Adverse Conditions Table

The purpose of the adverse conditions table is to provide focus to the technology validation strategy. 

These adverse conditions are categorized into three types: failure, damage, and upset. Failure is defined as 

a system or component that does not work properly including degradation of performance. Damage is 

defined as a structure or component that is broken, and upset consists of pilot error and/or loss of control 

due to occurrences that cannot be regulated via aircraft technology.

The initial adverse conditions table found in the IRAC Technical Plan was updated by collecting 

accident and incident data gleaned from findings within the ASRS and NTSB databases. The intent was to 

call attention to damage conditions that occur frequently while providing insight on their severity and 

frequency (Ref. 2). 

Thirteen adverse conditions subtypes of significance were found. Suggested initial test conditions are 

provided in the table for each of the adverse condition subtypes. The severity and frequency of each 

subtype is provided also as a means of prioritizing the example damage conditions. Finally, applicable 

IRAC milestones are referenced. Table 1 is the updated adverse conditions table. 

TABLE 1.—INTEGRATED RESILIENT AIRCRAFT CONTROL ADVERSE CONDITIONS 

Adverse condition Adverse condition subtype Severity (frequency)

Failure: System/component does 

not work properly. Includes 

degradation of performance.

1. Landing gear 

 Nose wheel steering (NWS)

 Main gear and tire

 Anti-skid/braking system

 Gear extend/retract mechanism

Accident (1)

Incident (597)

2. Avionics

 Instrumentation/communication/ 

navigation 

 Flight management/monitoring 

system

 Weather radar

Accident (1)

Incident (347)

3. Electrical 

 Auxiliary power unit 

 Radar 

 Actuator wire breaks 

 Wire chafing

Accident (3)

Incident (25)

 
NASA/TM—2010-216226 4 

TABLE 1.—Concluded. 

4. Hydraulics Accident (0)

Incident (24)

5. Static and dynamic actuator failure 

effects (single actuator and multiple 

actuator failures)

Accident (n/a)

Incident (n/a)

6. Environmental control system 

 Pressurization system

Accident (1)

Incident (9)

Damage: Structure/component is 

broken 

1. Propulsion system 

 Throttle and/or power-level system 

 Engine 

 Engine icing 

 Fire 

 Fuel system 

 Vacuum pump

Accident (20)

Incident (182)

2. Control surfaces 

 Rudder, aileron, or elevator

Accident (8)

Incident (144)

3. Aerodynamic and structural damage 

(wing and/or tail)

Accident (1)

Incident (41)

Upsets: Consists of pilot error 

and/or loss of control due to 

occurrences that cannot be 

regulated via aircraft technology

1.Electrical Accident (3)

Incident (25)

2. Severe weather 

 Icing 

 Winds 

 Poor evaluation of weather

Accident (56)

Incident (n/a)

3.Inadequate attitude/airspeed, and/or 

stall/spin

Accident (36)

Incident (n/a)

4. Pilot 

 Improper use of controls 

 Inadequate training or experience

Accident (104)

Incident (n/a)

3.0 General Assessment of State of the Art of Flight Control 

This section includes a general overview of the current state of the art in adaptive flight control. The 

objective of this study is to provide an assessment of the state of the art as applicable to the adverse 

conditions identified in Table 1. Unfortunately, flight control technology is often not specific to a certain 

adverse condition. In the following section, research presented is specific to the adverse events but not 

necessarily specific to flight control. The reason for including this information is that there are many areas 

where state-of-the-art research in sensors or predictive algorithms may be very useful as inputs to 

adaptive control schemes that have yet to be designed. In addition to being a survey of the current state of 

the art, this study will hopefully spur new ideas on adaptive flight control. 

3.1 Compensating for Damage and/or Reduced Performance

One such use for this technology is as an adaptive flight controller that takes as input, information on 

damage or reduced utility of actuators and control surfaces, and calculates compensatory states for all 

working components (Ref. 6). A second example uses damage or reduced utility inputs and uses them to 

calculate a revised flight envelope to be used by the pilot (Ref. 7). A specific example of an adaptive 

flight controller with a neural network is a system installed on the NASA Dryden NF–15B Intelligent 

Flight Control System (IFCS) aircraft. Its adaptive flight controller is equipped with a neural network to 

simulate the effects of a locked stabilator. With one stabilator locked, a conventional command given by 

the pilot would result in a response that is different than expected. For example, a simple pitch command 

would not only cause reduced pitching movement because one stabilator is not working, but it would also 

cause a slight roll due to the asymmetry. The system was a direct adaptive system and was driven by 

feedback errors making prior knowledge of the failures unnecessary. The simulator was flown with three 
NASA/TM—2010-216226 5 

different scenarios. The first scenario was a control case that had no failure. The second had a locked 

stabilator with no adaptive control, and the third had the locked stabilator with the adaptive control 

algorithm. A pitch maneuver was attempted, and the simulator logged both the pitch and roll response of 

the aircraft. The adaptive control did improve pitch response, but roll was still induced (Ref. 8).

Another variant of adaptive flight control translates pilot control input into the proper throttle and 

control surface inputs while compensating for any damage conditions that may exist. One such system is 

called an EZ-Fly system (Ref. 9), which utilizes an artificial neural network. The system responds 

differently to stick and throttle commands than that of a normal aircraft. For example, the longitudinal 

movement of the stick commands the plane to a set flight path angle instead of a pitch angle, and the 

equivalent occurs with lateral movement. Thus, the pilot is not controlling the actual control surfaces but 

rather setting the angles at which he wants the plane to fly and letting the flight controller use the control 

surfaces accordingly. This lets the flight controller compensate for damage to control surfaces or actuators 

without any change in behavior from the pilot. In addition, the throttle is replaced with a speed lever, and 

the flight controller calculates the appropriate throttle positions. Simulator tests show that this system 

significantly increased the amount of occurrences where pilots kept control of the airplane when damage 

conditions were present as compared to conventional controls. This was especially true for inexperienced 

pilots that did not have as much training in regards to controlling a plane when not all the control surfaces 

are available (Ref. 9).

3.2 Retrofit Upgrades

Retrofit software upgrades to flight controllers of production aircraft exist that allow for fault-tolerant 

calculations to be made. For example, a structure learning modeling algorithm, which is a type of neural 

network, has been designed to retrofit an F/A–18 aircraft. The algorithm comes into play when faults in 

control surface or actuators, adverse weather, icing conditions, or other conditions are present, which 

cause the aircraft dynamics to differ from normal operations. The algorithm calculates corrections that 

will be applied to control inputs and attempts to return controls to a normal state. This was tested using 

both Boeing and Air Force pilots in simulated F/A–18 maneuvers (Refs. 10 and 11).

3.3 Flight Control Through Engine Response

In addition to calculating compensatory trims for a decreased set of available control surfaces, there 

has also been research into using variable engine response, adding another element to the available 

methods of control (Ref. 12). In some extreme cases, this method might even be one of the only options 

left for controlling an aircraft. Researchers at NASA Glenn Research Center and N&R Engineering have 

created a simplified dynamic model of a 40,000-lb thrust turbofan engine using a MATLAB/Simulinkbased linear model. Under various airframe damage scenarios, the model determines an appropriate thrust 

response (Ref. 13). 

3.4 Test and Validation of New Adaptive Controllers

Once new adaptive flight controllers are designed, test and validation become very important to the 

development process. Unless they are shown to increase safety, manufacturers will not want to include 

these new systems on new aircraft, and operators will not want to retrofit existing ones. As flight tests are 

extremely expensive, other methods of testing have been developed. Researchers at NASA Langley 

Research Center have developed the Airborne Subscale Transport Aircraft Research (AirSTAR) system, 

which is a collection of dynamic subscale models of various aircraft operated out of a control center. 

Damage conditions can be replicated in realtime, control algorithms can be loaded onto the system, and 

pilots can test adaptive control systems in a live setting (Ref. 14). The Massachusetts Institute of 

Technology has also developed an indoor facility for testing flight control technology on highly aerobatic 

unmanned aerial vehicles (UAVs). This facility is called the Real-Time Indoor Autonomous Vehicle Test 
NASA/TM—2010-216226 6 

Environment (RAVEN) (Ref. 15). The use of UAVs allows for flight control technologies to be tested in 

a real-time setting that is much less expensive than full-scale flight tests (Ref. 16). 

In addition to UAV testing, another less expensive option is through the use of flight simulators. One 

example of an extensive study utilizing simulators was conducted by a group of researchers in the 

Netherlands. The researchers reconstructed a 1992 accident that occurred in Amsterdam on a simulator, 

and had a number of pilots recreate the scenario with various fault-tolerant adaptive control strategies 

available. The researchers conducted the scenarios with both manual control from the pilots and 

automatic control. In both cases it was very clear that catastrophic events can be potentially avoided 

through the use of these automated flight control algorithms. The pilots noted that in most cases the 

control of the aircraft was very comparable before and after the failure. The study also asked pilots what 

was most important for future improvements. It was noted that the determination, presentation, and 

possibly the protection of the safe flight envelope could be improved upon (Ref. 17).

The next section goes into more detail in relation to each of the adverse conditions cited in Table 1. 

Because many of the possible adverse conditions in the table do not have specific flight controllers 

designed for them, the summaries also include state-of-the-art detection technologies, predictive 

algorithms, or other information that might be useful for input into a new adaptive flight control 

algorithm. 

4.0 Flight Control Systems and Technologies in Relation to Specific 

Adverse Conditions 

This section contains the results of the literature survey conducted to document the state of the art in 

flight control systems and technologies as applicable to the 13 adverse conditions listed in Table 1. The 

findings for each adverse condition are summarized below.

4.1 Adverse Conditions Involving Failure of an Aircraft System or Component 

4.1.1 Landing Gear

Although faults in the landing gear and braking systems do not cause loss of control in the air, they 

are responsible for a significant number of accidents and incidents on the ground. If faults are known in 

advance, it might be possible to design a flight control system that could inform the pilot to alter his 

landing technique to decrease the risk of losing control of the aircraft upon landing. The current methods 

used by commercial airlines in regards to monitoring the health of landing gear mostly relate to routine 

maintenance and visual inspections. Additional maintenance may also be performed based on flight data 

or pilot observations. For example, a perceived hard landing by the pilot might warrant some extra 

maintenance procedures. The problem with this method is that some operational scenarios that put larger 

than normal loads on the landing gear may not be reported if the pilot does not think of the scenario as a 

hard landing. Research is being conducted to design a load sensor to be able to monitor this information 

(Ref. 18). Currently, structural sensors are not integrated into any landing gear systems. Even if a scenario 

causes some sort of damage to the structure, and it does get reported, it is possible that a visual inspection 

will not find the problem. For example, some stress fractures to the structure may not be visible without 

disassembling the main gear. A technique called acoustic emission is being developed that could detect 

faults in structures, breakage of seals, or other problems. During inspection, the technician uses a device 

to propagate acoustic waves, and sensors pick up potential variations in the waves caused by faults 

(Ref. 19). Another problem that occurs in landing gear is shimmy during taxiing and landing. Shimmy is 

an oscillation of the structure that is normally caused by rough runways and is usually around 10 to 

30 Hz (Ref. 20). Although shimmy does not usually cause catastrophic failures, it can cause excessive 

wear over time. Current methods for dealing with shimmy include physical dampers, but this cannot adapt 

to changing situations. A new adaptive model under development uses sensors to detect shimmy, and then 
NASA/TM—2010-216226 7 

calculates the taxiing velocity and yaw angle to minimize the shimmy, and gives this information to the 

pilot (Ref. 21). Currently, no production aircraft have this technology.

Conventional anti-skid systems consist of sensors that are used to detect when wheels are locked, and 

the brakes are pulsed accordingly. Better algorithms in the anti-skid system control unit could help 

decrease skids and stopping distance and increase reliability. There are a number of researchers working 

on developing these algorithms for use in anti-skid controllers (Ref. 22). There are also units that are 

designed to detect potential faults in the braking system. By utilizing braking data, an anti-skid control 

unit produced by General Atomics has the capability to detect anomalies in actual braking performance in 

contrast to expected performance. These anomalies act as a red flag for potential faults in the system 

(Ref. 23). Although this helps detect potential failures as they occur, there is not much on the market to 

detect early stages of failure. The products on the market, such as the controller from General Atomics, 

are backward looking and the mitigation is based on maintenance. Some research is being conducted in 

regards to a brake monitoring system that will offer some prognostic capabilities. This system involves 

using sensors to monitor wear on friction surfaces and pressure sensors to monitor conditions in 

hydraulics. As anomalies from normal sensor data become apparent, the system reports to a user interface 

that the risk of failure is high for a specific component (Ref. 24). Although this technology does not 

include fault mitigation, it can help increase operator awareness of conditions and decrease costs by 

reducing the need for frequent routine maintenance.

4.1.2 Avionics

One way that faults in instrumentation and navigation equipment can indirectly cause a pilot to lose 

control of an aircraft is if the pilot does not know that the equipment is faulty. If they lack this knowledge, 

and continue to rely on the equipment, they may have an incorrect perception of their current flight 

conditions and perform a maneuver that causes them to lose control. Another common problem related to 

faults in instrumentation, communication, and navigation is that faults are often noticed by the flight 

crew, but detection systems report that “no fault is found” (Ref. 25). An intelligent flight control system 

that was aware of these faults could report them to the flight crew, hopefully preventing potential 

accidents or incidents. Current methods of detection are similar to those of wiring chafing (which is 

discussed in the next section), in which impedance is measured for various electronics, and variations 

from the norm are detected as faults. The problem is that many times the variation in impedance is too 

small to be detected. One method that is promising in regards to finding these faults is called pulsearrested spark discharge (PASD), which can be used to detect faults in electronics that cannot be detected 

using impedance methods (Ref. 26). In particular to navigation, the increased use of Global Positioning 

Systems (GPSs) has made faults in onboard navigation equipment much less relevant, as it uses data 

transmitted via satellites as opposed to electronics integrated with the rest of the aircraft. Along these 

same lines, utilization of wireless networks onboard could reduce the reliance on physical wiring in the 

future, which would eliminate many of the current failure modes (Ref. 27). Technologies related to 

weather systems in particular will be discussed later in this report.

4.1.3 Electrical 

Over time, the insulation in wires that are near structures or moving parts can be degraded by friction. 

If the insulation is entirely rubbed off, it is possible for the wire to cause a short in the electrical systems. 

If these faults (or risk of fault) were able to be detected, an adaptive control system might be able to be 

designed to selectively isolate these areas from the rest of the system to protect electrical equipment from 

shorts. The most common technique to detect wire chafing in current aircraft is through visual inspection. 

This problem occurs increasingly as aircraft fleets age, and 43 percent of electrical system mishaps are 

related to connectors and wiring (Ref. 28). As we move towards more electric aircraft, the increased 

amount of wiring will only make this problem more important. Wire placement is a big driver of these 

problems, as wire bundles closer to hydraulics or airframe structures are more prone to chafing. One 

simple method of mitigation is to use standoff clamps to hold wire bundles away from these structures 
NASA/TM—2010-216226 8 

(Ref. 29). Some more advanced methods of detection use differences in impedance to detect flaws in 

insulation before the conductor inside is harmed and shorts occur. However, one drawback to this method 

is that it does not help locate the area of the problem on the wire (Ref. 30). A company called Innovative 

Dynamics planned an integrated test of a wire chafing sensor in a Goodrich wiring harness. As the sensor 

becomes chafed, it should make maintenance technicians aware of the problem before the actual wires are 

harmed (Ref. 31). 

Another detection method that is especially promising because it can be used to both detect and locate 

faults in a wire harness is PASD. This method can find faults that would only produce undetectable 

changes in impedance and can be used for multiple other purposes in addition to detection of wire chafing 

(Ref. 32). Another new type of sensing technology involves wrapping fiber optic cables around a wire 

bundle. As the fiber becomes chafed or breaks, the decreased transmission or short is easily detectable, as 

well as the location on the wire bundle (Ref. 33). As electric aircraft technology advances, wireless 

networks may be able to eliminate a lot of these problems (Ref. 27). Previously introduced robust laser 

interferometer (RLI) technology also demonstrated potential for electrical wiring health monitoring. 

Finally, previously introduced time domain reflectometry (TDR) is another method that can be used to 

measure changes in electronic wiring interconnect system characteristic impedance for detection of chafe, 

nicks, and corrosion defects (Ref. 34).

Monitoring the deterioration of components in electronic systems is much harder than mechanical 

systems (Ref. 35). There has been a thrust in recent years in developing methods and technologies for 

prognostics and health management of electronic components such as power system components, 

avionics, solder joints, etc. For diagnostics of electronic components expert systems, neural networks and 

fuzzy logic systems are used (Ref. 36). Bayesian diagnostic models are also used for diagnosis of faults in 

electronic systems (Ref. 37). 

Solder joint faults are proving to be one of the main failure sources of electronic components. Thus, 

solder fatigue modeling and monitoring technologies are used to monitor small cracks that develop and 

propagate in the microstructure of solder joints due to thermal loading and/or aging. The changes in the 

material microstructure due to aging are used by some researchers to predict remaining useful life of the 

solder joints of electronic components (Refs. 38 and 39). Other prognostic methods have been proposed 

for various critical electronic components including digital electronic boards (Ref. 40), silicon carbide 

packaging (Ref. 41), integrated circuits (Ref. 42), and power actuators (Ref. 43).

4.1.4 Hydraulics 

Hydraulic systems are extremely important in regards to flight control, as they play a role in 

positioning the control surfaces in many aircraft. Therefore, a failure in the hydraulic system can prevent 

the pilot from having full control of the aircraft. Another area where hydraulic systems are used is in 

commercial aircraft landing gear. A failure in this system can cause problems when the pilot wants to 

extend or contract the gear. Linked closely with hydraulic systems that maneuver the control surfaces are 

actuators as they are often driven by hydraulics. One type of actuator is the electromechanical actuator. In 

normal operation these are driven by electric power, but in high-stress situations, during a jam, or when 

electric power to the actuator is disrupted, the hydraulic system supplies the power needed to operate the 

actuator. 

One problem that can occur in the hydraulic system is called cavitation. This phenomenon occurs 

when the pressure at the pump inlet is lower than the vapor pressure of the fluid, and it causes bubbles to 

form in the system. It is ideal to avoid this situation if possible, as it increases wear on the system. 

Researchers have developed a neural network based algorithm to detect cavitation using as inputs the 

electric current supplied to the pump motor and a voltage that can be used to determine the operational 

speed and output flow of the pump (Ref. 44). The cavitation data could possibly be fed to an adaptive 

control system to help increase the operational life of the hydraulic system. Another method of 

monitoring the health of hydraulic systems involves a neural network scheme that performs a vibration 

analysis of hydraulic pumps. The algorithm uses the fundamental frequencies and harmonics of the pump 
NASA/TM—2010-216226 9 

and determines if it is operating normally or in a degraded state (Ref. 45). This could be used in an 

adaptive control scheme to inform the pilot or maintenance technicians of the health of the system. 

There are many types of hydraulic system faults that result in a small leak somewhere in the system. 

Small leaks can be present for a while before they cause larger problems, but if they are detected early it 

is possible to prevent these problems. One method for detecting these faults involves online model-based 

fault detection. Using the operational speed of the pumps, it is possible to determine the pressures that 

should exist at various points in the system. Models have been developed that use pump data and pressure 

sensors to determine if there are leaks in the system and their location (Ref. 46). Another part of the 

system that cannot be overlooked is the health of the tubes that carry the fluid. Some of the research in 

this area involves modeling the decrease in operational lifetime of tubes with scratches or other damage 

(Ref. 47). 

4.1.5 Static and Dynamic Actuators 

Very similar to the previous section on hydraulic systems in general, actuators play an essential role 

in adaptive control technology as they move the control surfaces. As discussed, the state-of-the-art 

technology in terms of actuators involves the use of electromechanical systems. One problem that can 

occur in these actuators is called ball jam where the moving part containing the ball bearing gets stuck 

somehow, preventing the control surface from moving. Detecting actuator failures would provide 

valuable input to adaptive flight control software, as it would help establish the set of useable control 

surfaces. The majority of detection and prognosis technology in regards to ball jam in electromechanical 

actuators (EMAs) is model based. Various modeling techniques are used to detect malfunctioning EMAs, 

as well as predicting the time to fail for the entire system (Ref. 48). Although models have been created, 

the majority of the work is still done by technicians on the ground. However, technologies are being 

developed that use flight control data and will be able to deliver gray-scale, as opposed to pass-fail, 

feedback to maintenance technicians. With this information, maintenance technicians will be better able 

to isolate problem areas with less chance of overlooking problems or dealing with false-positive responses 

(Ref. 49). Another technology being developed is hybrid electromechanical/hydraulic actuators, which 

allow for in-flight mitigation of ball jam. These actuators run mechanically in normal operation, but a 

parallel hydraulic system kicks in when extra power is needed because of heavy loads or a ball jam. It is 

not certain that the hydraulic system will fix the jam, but it is one method of in-flight mitigation (Ref. 50). 

4.1.6 Environmental Control System 

Although faults in the pressurization system might not be a direct cause of many accidents and 

incidents, it can have adverse effects on the flight crew, indirectly affecting their ability to control the 

aircraft. Currently, pressurization systems use either preconditioned air from the ground or bleed air from 

the engine, which requires a water separator. It is also noted that 61 percent of component failures in this 

system are related to the water separator, so a significant amount of risk can be reduced by focusing on 

this component. However, in the future this failure in particular may have a decreased relevance. As 

aircraft manufacturers move towards more electric aircraft, such as the Boeing 787, they may use electric 

fans to pressurize the cabin where no water separator is required. While these faults are eliminated, they 

would then need to focus more attention on electrical and wiring faults, and the power plant will need to 

produce four times as much electricity (Ref. 51). The most advanced technology in regards to air 

conditioning and pressurization faults is actually related to predicting the time to failure for components 

such as the water separator. This is in contrast to most cases, where detection and diagnostics are more 

advanced. Researchers have produced fairly extensive prognostic models predicting the failure of various 

components in the pressurization system using Weibull distributions. These distributions are based on a 

power law relationship in which the probability of failure increases with flight hours (Ref. 52). 

Information from these algorithms might be used to give the flight crew a metric that measure “risk of 

failure” for this system. 
NASA/TM—2010-216226 10 

4.2 Adverse Conditions Involving Damage to a Structure or Component 

4.2.1 Propulsion System 

The propulsion system is an area of the aircraft that has always attracted a significant amount of 

research. One area that has specific applications for adaptive control technology is the fuel system. The 

majority of fuel system faults occur because of water condensation, which can be harmful in freezing 

temperatures, degrade performance in other situations, or clog filters due to sediment. There are very few 

diagnostic systems specifically for fuel systems and even fewer systems that can mitigate detected faults. 

The conventional method of fuel system management is via a central computer system that controls fuel 

distribution among various tanks according to the stage of flight. Researchers at Penn State University 

have developed an adaptive fuel filtering system with parallel pumps. Data is collected via pressure 

sensors and can be used to identify faults or clogged filters. Integrated logic can adjust the flow or use 

water from the water separator to back flush clogged filters. A successful integrated test of this system 

was performed using a test rig constructed with diesel engines (Ref. 53). Multiple groups are researching 

alternatives to the central computer system for fuel management that use distributed networks of sensors 

and microcontrollers. The microcontrollers are used to control the fuel pumps using input from the sensor 

network in order to achieve the desired distribution of fuel in the tanks. This network of smart sensors 

eliminates the need for a central computer (Ref. 54). One method of decreasing risk of fires in the fuel 

tanks is to replace electromechanical sensors, which have a higher risk of initiating combustion in 

flammable environments, with fiber optic sensors (Ref. 55). Another technology to decrease fuel-systemrelated fires involves detection of electrical faults in the fuel pumps. A retrofit device called a universal 

fault interrupter was approved for use on Boeing 737–NG and 757 aircraft in 2008. It is an electrical box 

that is installed adjacent to the electrical relay and can detect electrical faults in the fuel pump and will 

shut down the pump when the inlet is uncovered or in times of uncommanded pump operation (Ref. 56).

Bearing health is critical to the performance of turbomachinery. Diagnostic technologies for rolling 

elements are relatively well developed. Among these are stress wave analysis (SWAN), which provides 

real-time measurement of friction and mechanical shock in bearings. This high-frequency acoustic 

sensing technology filters out background levels of vibration and audible noise and provides a graphic 

representation of machine health (Ref. 57). Acoustic emission technologies are also utilized as an 

indicator for bearing stress. RLI, an alternative technology to acoustic emissions, has also demonstrated 

use for bearing health monitoring (Ref. 58). On the other hand, accurate prediction of remaining life of 

bearings remains a challenge. Novel techniques for remaining useful life (RUL) estimation for bearings 

are in development especially those targeting incipient faults (Refs. 59 to 62). “Smart bearings,” although 

still at development stage, is another novel technology. If successfully developed, these bearings will 

have a number of unique features such as having all sensory/telemetry data built into the bearing, and 

durability for higher temperatures, long life, and compatibility with the operating environment (Refs. 63 

to 65).

The oil and lubrication system can have a part in causing significant damage in the propulsion system. 

The main problems associated with aircraft oil and lubrication systems arise from clogged filters, pressure 

anomalies, and water in the oil. Pressure anomalies can result from multiple causes including low levels 

of lubricant and clogged filters. The health of this aspect of the lubrication system is monitored by 

pressure sensors that relay information to the pilot, and pilots are trained to recognize and adjust for 

various situations (Ref. 66). Using data from pressure sensors, adaptive control schemes could be 

designed to recognize the cause of various anomalies and either adjust aircraft operation automatically or 

provide guidance to the pilot. Another method that might be used to monitor the loss of lubricant in an 

engine involves a vibration analysis of moving engine components. Different vibration signatures arise as 

the level of lubricant in the system varies. This method is currently used by the U.S. Army for light 

armored vehicles but might also be applied to aircraft (Ref. 67). Faults in the oil system, such as a clogged 

filter, are often indicators of failures in components lubricated by the system. For example, as a moving 

part is worn down over time, particulates accumulate in the oil that lubricates it, and this can clog the 
NASA/TM—2010-216226 11 

filter. Also, more than just getting worn down, structural failures in components might cause larger pieces 

of debris to accumulate in the system. By analyzing the oil in the system for composition and size of 

particulates, information about the health of lubricated components and of the oil system itself can be 

obtained. In 1995, the U.S. Army mandated that its aircraft undergo this type of analysis on a regular 

basis (Ref. 68). A company, Jet-Care, has commercialized the process by charging operators to send in oil 

and filter samples for analysis. They send back results that include a diagnosis of system health and a 

prognosis for suggested maintenance to provide (Ref. 69). If real-time monitoring of engine oil 

particulates and debris were available, algorithms could be designed to recognize the wear state of various 

components and give this information to the pilot. 

One control system that has caused problems in the propulsion system is the full authority digital 

engine control (FADEC) system. On some aircraft, FADEC software triggers a fault when excessive force 

is applied to the throttle. When this occurs, the fault mode holds the thrust level at the last known setting. 

In various cases where this occurred at full power, pilots have had to shut down engines in order to reduce 

thrust. One company that was affected by this problem was Eclipse. They incorporated software updates 

into their legacy fleet to fix this problem (Ref. 70). The very existence of a FADEC allows for adaptive 

controllers to use this engine control software to help control an aircraft when control surfaces fail. There 

have been a number of algorithms written that use variable engine operation to control the aircraft 

(Ref. 71). Another component of the propulsion system that has been known to cause problems is the 

vacuum system. In some aircraft, the vacuum system is responsible for powering instrumentation such as 

the attitude indicator. If a failure in the vacuum pump is not known to the pilot, he may still rely on the 

attitude indicator even though it is no longer displaying accurate information. In certain circumstances 

this can lead to a loss of aircraft control. If the failure is detected, and the pilot is aware of this failure, 

loss of control could possibly be avoided. One method for detecting faults in the vacuum system involves 

measuring vibration data from the bearings in the pump and comparing it to data from normal operating 

conditions. Anomalies can be a sign for a fault in the system (Ref. 72). 

Another adverse condition in the propulsion system that can lead to a loss of control is an engine fire. 

The conventional method of fire extinction/suppression in aircraft engines involves discharging halon. 

Researchers are looking for alternatives to halon because of its environmental impact, however, many 

think it will not be feasible to replace on many legacy aircraft. One area of research involves designing a 

nacelle with fire-resistant composites (Ref. 73). Boeing has designed a system in which overheat sensors 

on various engine components are used to detect potential engine fires. A warning light and/or sound are 

triggered in the cockpit to alert the pilot, who is then able to disperse fire suppressant via a switch 

(Ref. 74). There have also been numerous studies that have used computational fluid dynamics (CFD) 

modeling to examine how droplets of fire suppressant will flow through an engine nacelle that include 

obstructions such as wire bundles, support structure, and other objects (Ref. 75). Similar efforts have 

investigated the spread of engine fires throughout the nacelle (Ref. 76). The results of these efforts could 

be used to design a control algorithm for a fire suppressant system. Based on the location of the fire (from 

sensors), and the most probable paths of suppressant and spread of the fire accounting for obstructions, an 

algorithm could decide the optimal discharge of suppressant from a multinozzle system. 

4.2.2 Control Surfaces 

There is a significant amount of technology currently being utilized for control surface fault detection. 

Frequently, the faults associated with control surfaces occur because of problems with the actuators or 

hydraulic systems that move the control surfaces and/or keep them in position. Excessive loads can also 

cause structural damage including cracks and fractures. The presence of one of these faults can mean that 

the control surface gets stuck at a certain trim, is more difficult to position, or becomes completely 

ineffective. Algorithms have been developed that use flight variables to detect these types of faults 

(Ref. 77). Once a fault is detected, flight control software can diagnose the specific component where the 

problem exists. Some systems have been designed that give this information to the pilot. More advanced 

algorithms have been developed that estimate the performance degradation and a new flight envelope in 

the presence of the failure. One algorithm of this type that was meant for UAVs also determines whether 
NASA/TM—2010-216226 12 

the mission can be accomplished with the degraded performance or whether the mission must be aborted 

(Ref. 78). Additional research has been done that is specifically focused on the performance of autoland 

systems in the presence of a control surface fault. Because of the precision needed at landing, this phase 

of flight is particularly susceptible to problems resulting from loss of control caused by control surface 

faults (Ref. 79). Numerous groups have designed flight control software packages that incorporate neural 

networks for additional capabilities to be used in landing with control surface faults. Another interesting 

topic being studied is the effect of false identification of faults on performance. If a flight controller 

detects a control surface fault, but the control surface is working properly, the compensatory trims it 

calculates for the other control surfaces will actually cause degraded performance themselves. Currently, 

the focus of this research on fault detection is merely calculating the effects of this phenomenon and not 

working on any mitigating solutions.

4.2.3 Aerodynamic and Structural Damage 

Just as faults in control surfaces can cause the conventional flight control inputs to produce unwanted 

maneuvers, structural damage to the aircraft can also cause unwanted aerodynamic effects. If these 

anomalies were known, they could be used as input to an adaptive control system to calculate the proper 

adjustments to control surface trims. The current state of the art in detection methods for fatigue cracks on 

the metallic airframe structure primarily involves on-ground inspections. In terms of current fleet 

operation, this assessment is performed manually as a part of routine maintenance, which means a crack 

might be present for a period of time before it is identified. Various probe technologies are currently 

being investigated that would allow technicians to detect cracks that would not otherwise be visible. One 

such technology is eddy current inspection (ECI), which is a new method that produces a magnetic field 

on a material’s surface to detect cracks, corrosion, heat effects, and thicknesses. ECI is effective on 

metallic surfaces; however it is limited to the area directly below the instrumentation point (Ref. 57). For 

example, one probe uses eddy currents to detect fatigue cracks underneath fasteners (Ref. 80). For 

onboard detection, piezoelectric transducers (PZTs) could detect a crack via disruptions in stress waves 

sent through particular airframe structures in flight (Ref. 57). However, the feasibility of integrating this 

technology onto an aircraft is yet to be determined (Ref. 81). Acoustic emission (AE) is another 

technology that is used for surface and inner structure crack detection and localization. This 

nondestructive evaluation technique involves monitoring for the emission of high-frequency vibration 

(>100 000 Hz) as an existing structural defect (crack) is stressed from the static loading of the system 

(Refs. 64, 65, and 67). In addition, induced positron analysis (IPA) technologies can reliably detect and 

quantify tensile plastic strain damage induced by simulated and operational conditions in aerospace 

material specimens and components including metallic surfaces (Refs. 82 and 83). In the area of 

prognostics, numerous algorithms have been developed to predict the growth of cracks under various 

flight conditions (Ref. 84). Once a crack has been discovered, the current state of the art in mitigation 

normally involves patching and/or bonding with epoxy or selective reinforcement (Ref. 85). Some 

additional research has been done in terms of effective selective reinforcement and what type of fixes can 

be implemented to best slow the crack (Ref. 86). Still, none of these methods involve onboard solutions. 

The closest there is to an integrated solution does not really involve condition-based mitigation, but the 

use of materials through which cracks naturally propagate more slowly (Ref. 87). One method of 

conditional-based mitigation is dynamic controls that change the load spectra to lighten loads on fractured 

structures. This does not fix the crack but would help slow its progression until it could be fixed on the 

ground (Ref. 88).

The use of composite materials in structural design is becoming increasingly popular in aerospace 

applications because of the benefit of reduced weight without much compromise in strength and stiffness 

performance. Their increasing use has underlined the need to understand their principal mode of failure, 

delamination (Ref. 89). Delamination may occur in the form of microcracks and voids that usually leads 

to a macroscopic loss of stiffness and strength, which and may lead to a catastrophic structural collapse. 

Some technologies for detection and diagnosis of delamination encompass sensors that detect 

unanticipated events such as impacts, sensor technologies for detecting aging, and nondestructive 
NASA/TM—2010-216226 13 

inspection (NDI) tools for flaw identification and damage characterization. Specific sensors developed for 

onboard impact detection include PZTs and fiber Bragg grating (FBG) ultrasonic structural health 

monitoring (SHM) sensors, which can locate the point of impact (Refs. 57 and 90). Once impact is 

detected, NDI tool images are used to study the damage region, and modally selective sensors (i.e., lamb 

wave sensors (Ref. 91)) are used to monitor further damage growth. Different damage growth prediction 

methods are finally employed to estimate the remaining lifetime of the structure. These include the linear 

elastic-fracture mechanics method, the cohesive-fracture model, and delamination-threshold load method 

(Ref. 92). Feature-based signal processing methods and data-driven classification techniques are also 

proposed for damage detection and prediction (Refs. 93 and 94). 

4.3 Adverse Conditions Involving Control Upsets Related to Pilot Error or 

Another Cause That Cannot Be Regulated by Aircraft Technology

4.3.1 Electrical 

Both military and commercial aerospace systems are becoming increasingly dependent on electrical 

power as systems move towards the more electric aircraft concept. This novel architecture relies on 

digitally controlled power distribution to provide power to flight critical subsystems such as avionics, 

fuel, etc. This increasing dependence on electrical power necessitates the development of new 

technologies for autonomous health management of electrical power systems. Power system faults cover a 

wide range of problems, some of which is covered under power electronics faults and wiring problems. 

So, in this section, the main focus will be on technologies developed for arc prevention and power 

management.

The arcing of electrical powered systems is a major safety concern to new and legacy aircraft. The 

advent of high-voltage direct current (dc) systems accentuates this problem. Thus, arc fault prevention 

methods and algorithms address this critical need for electrical power system prognostics and health 

management. Arcing faults occur as a result of chafing and cracking of insulation, dielectric breakdown, 

and looseness at terminal connections. Once arcing is initiated, damage may propagate to other 

conductors in the wire bundle. The discharge of arcing energy results in insulation damage, smoke events, 

the loss of adjacent wires in a wire bundle, and ignition of flammable materials and vapors. Such 

conditions have been estimated to result in approximately one unscheduled landing during an average day 

of air traffic worldwide and are the primary suspects in a number of catastrophic events. The most 

common technique used for arc fault prevention is thermal circuit breakers. Newer technologies 

developed for arc prevention include the arc fault circuit interrupting (AFCI) technology (Ref. 95), which 

relies on the use of arc fault circuit breakers as a supplemental protection against arc fault conditions in 

addition to the thermal overload protection provided by present generation circuit breakers. Solid-state 

devices are also proposed as a potential replacement for traditional thermal circuit breakers as a way of 

preventing arcing faults. These devices typically have longer life cycle, faster response time for 

overloads, and lower power dissipation when compared to thermal circuit breakers (Ref. 96).

There are also technologies developed for management of digital power distribution in general and 

more specifically, arc fault management. An example is the Aircraft Electrical Power Systems 

Prognostics and Health Management (AEPHM) program (Ref. 97) sponsored by the Air Force Research 

Laboratory (AFRL). Other arc fault management systems can monitor the operation of the loads. 

Examples include monitoring marker lights to flag failures, monitoring motors and actuators for 

acceptable current levels, and logging the time and duration of operation of loads (Ref. 96). Distributed 

power systems are another new technology. This technology is an alternative to traditional centralized 

power systems. It is based on locating the power control devices near the electrical loads to reduce the 

amount of power wiring. The control commands for this type of configuration are sent to the power 

control devices with a data bus. Making use of a distributed power distribution unit not only significantly 

reduces wire weight, but can lower installation and maintenance costs based on the reduction in the 

number of connections. 
NASA/TM—2010-216226 14 

4.3.2 Severe Weather 

Icing is one of the adverse events that has attracted a significant amount of research, spanning a range 

of interests for health monitoring and adaptive control. Starting with detection, the state of the art includes 

both the detection of icing conditions in the airspace using ground-based models, as well as in situ 

detection of ice accretion on the aircraft. Researchers at the National Center for Atmospheric Research 

(NCAR) have developed the forecast icing potential (FIP) algorithm, which uses data from the National 

Center for Environmental Prediction to calculate icing potential in the three-dimensional environment 

(Ref. 98). If integrated into the flight deck, this could be used by pilots to avoid areas of high icing 

potential. Another method of detection is a joint venture between NASA and the U.S. Army to use 

ground-based Ka- and X-band radar to locate icing conditions in cloud formations, but this has not yet 

been integrated into any flight systems (Ref. 99). One method currently being investigated for detection 

of ice accretion on the aircraft is TDR. With this method, electromagnetic waves are transmitted, and 

when they are received, the presence of ice is detected based on the dispersion on the waves. The 

thickness is also able to be determined. Currently, this technology is still in the feasibility study stage 

(Ref. 100). In terms of prognosis, there are numerous computational models that are used to predict the 

growth of ice both on the airframe and on surfaces in the propulsion system (Ref. 101). In addition to 

prognostic models of ice accretion, models also exist to predict the degradation of flight controls as icing 

conditions develop (Ref. 102). This type of data could be especially useful for flight control algorithms. 

In terms of mitigation, one method that has been found to be effective is actually a software change 

introduced by General Electric (GE) in 1996. They found that an engine flameout due to icing frequently 

occurred on descent between an altitude of 20 000 and 10 000 ft. GE changed the electronic control unit 

variable bleed value (ECU VBV) logic to increase ice extraction from booster core/inlet in this range of 

altitudes, and no events have occurred on aircraft where this change was made (Ref. 103).

There are currently many systems under development that may prove more useful to pilots than 

conventional weather radar in the cockpit. One system under development takes in information from 

various radar sources to provide an in-cockpit display of turbulence risk. One of the sources is the 

enhanced-turbulence (E-turb) radar that has been developed to detect all turbulence earlier than 

conventional weather radar (Ref. 104). Another technology under development is the Turbulence 

Autopilot Reporting System (TAPS). When the accelerometer on an aircraft equipped with this 

technology senses that turbulence is affecting flying conditions, it sends this information to computers on 

the ground where the severity of the turbulence can be calculated and sent out to in-cockpit displays on 

other aircraft (Ref. 105). Information from E-turb and TAPS is integrated into a single display that shows 

the risk of turbulent conditions and provides the pilot with more information than available with 

conventional weather radar. In 2002, an algorithm called the integrated turbulence forecasting algorithm 

(ITFA) was demonstrated. It is similar to the previous system in that it integrates various sources of 

turbulence data, but different in that it combines forecasting algorithms while the previous system used 

radar and aircraft performance data. This algorithm combined the capabilities of several turbulence 

forecasting algorithms into one code, and was tested with a group of meteorologists from United Airlines 

and dispatchers from Comair. It was found that the algorithm had a high level of usability, but that it had 

a fairly high rate of false positive predictions or predicted that severity was higher than the conditions that 

were found (Ref. 106). The output from these turbulence forecasting algorithms may possibly be used as 

input to flight control algorithms that calculate alternate trajectories and flight paths for pilots to better 

avoid turbulence. Algorithms have also been developed to estimate the reduction in airspace capacity due 

to adverse weather conditions. This will allow air traffic controllers a systematic way to adjust traffic 

patterns in areas where adverse weather is detected (Ref. 107). The Boeing National Flow Model of 

airspace traffic was used to measure the benefits of automation and improved weather forecasting when 

airspace capacity constraints are in place due to adverse weather. The concept evaluated was 

“Collaborative Flow Management,” which lets airlines optimize their own schedule using ground delays, 

cancellations, rerouting, or other means (Ref. 108). 
NASA/TM—2010-216226 15 

4.3.3 Inadequate Attitude/Airspeed and Stall/Spin 

A large number of possible faults involve the propulsion system and can lead to inadequate 

attitude/airspeed and stall/spin. If these conditions are detected, an adaptive flight control system can 

assist the pilot on returning to normal flight conditions. Technologies developed to monitor and prevent 

engine stall and faults in turbomachinery cover a wide variety of techniques. These include monitoring of 

specific vibrations in an individual blade to prevent the potential of catastrophic failure and prevent 

turbine downtime. Specific technologies for blade vibration monitoring include eddy current, optical, and 

capacitive sensors and algorithms that can process and fuse data from these sensors. Eddy current sensors 

are used for the purpose of gas turbine engine stability monitoring including stall detection, and blade 

harmonics. Microwave blade tip sensors are another technology that has considerable promise as a state 

awareness technique for the monitoring of rotating blades and disks. These sensors produce informationrich waveforms of the blade end geometry. Foreign object damage (FOD) detection systems entail a suite 

of new technologies. These include systems for detecting and analyzing ultrasound or stress waves 

emitted when an object enters the intake of a turbine engine and impacts one or more of the blades in the 

engine. Upon detection, the FOD detection system can immediately inform the operator, inform another 

electronic device (computer, etc.), and/or latch the event for review by maintenance personnel. The Joint 

Strike Fighter Prognostics and Health Management (JSF PHM) Program employs this technology.

Debris monitoring is another class of technologies applicable to engine health monitoring. The 

fundamental principle of gas path debris monitoring is to sense the electrostatic charge associated with 

debris present in the gas path of jet engines or gas turbines. Gas path debris monitoring technologies are 

critical for propulsion health management. Engine Distress Monitoring System (EDMS) and Ingested 

Debris Monitoring System (IDMS) are two technologies demonstrated on the Joint Strike Fighter (JSF) 

seeded fault engine test (SFET) program. Tomography is another technology based on hyperspectral 

absorption spectroscopy for temporally and spatially resolved temperature and water concentration 

measurements in practical combustion devices.

Surface acoustic wave sensors are another technology with demonstrated application to turbine 

engines. These sensors can be used as multifunctional temperature/pressure sensing devices for turbine 

engine test validation. Finally, the development of harsh environment sensors is also critical to engine 

health management. Notable technologies in this class include silicon carbide sensor devices that can 

work at high temperatures up to 500 C. These sensors can be used for sensing motion, acceleration and 

gas flow, gas composition, and radiation detection. Another harsh environment sensor development for 

propulsion system applications is ceramic sensors, which are not limited thermally when compared to 

traditional metal thin film sensors.

4.3.4 Pilot 

Technology in the cockpit can play a large role in helping a pilot keep an aircraft in control, as well as 

help regain control if it is lost. For example, if a pilot is not aware of a failure that affects control surfaces 

or actuators, the flight envelope can be altered and a maneuver that would be within the limits of normal 

operation may cause a loss of control. If a pilot does not know that a control surface on one wing is stuck, 

they may use the control in an improper manner. The procedure to command the plane to pitch up might 

cause pitch that is less than desired, and also a slight roll. In one study of adaptive flight control systems, 

a pilot survey indicated that two improvements pilots were interested in involved calculation of flight 

envelopes and better in-cockpit information displays of this type of information (Ref. 17). Training can 

also reduce pilot error. The FAA released a report that stated that a lower level of English language 

proficiency can lead to communications problems in English-speaking airspace, and this has led to a few 

accidents. More stringent language requirements for pilots flying in areas where the language used by air 

traffic control is not their primary language would mandate more training for less proficient speakers and 

eliminate accidents (Ref. 109). Other studies have been conducted that show that training can lead to 

pilots using in-cockpit technology to a greater benefit. One FAA report studied the differences in pilots’ 

usage of weather radar before and after training. With no training, the pilots fell into two distinct groups. 
NASA/TM—2010-216226 16 

The first group used the weather radar to stay as far away as possible from adverse weather. The second 

group exhibited a potentially hazardous behavior in which they used the weather radar to keep their 

deviation from their planned flight path as small as possible. The latter is potentially hazardous behavior, 

as weather conditions are unpredictable and can change very quickly. After training, the prevalence of 

this behavior was only 44 percent of the original amount compared to a control group, and the average 

distance from the storm for the pilots that took the training also increased by a factor of 3 (Ref. 110). A 

possibility for new adaptive control technology is highlighted by this study. It might be possible to design 

a flight control algorithm in conjunction with the weather radar that calculates optimal alternative flight 

path for the pilot when adverse weather is experienced. This optimization would keep pilots from being 

overly conservative in their avoidance of adverse weather, but also hopefully prevent them from being too 

aggressive.

5.0 Conclusion

A general discussion of state-of-the-art technologies in flight control was presented. A more specific 

look at the state of the art of flight control as related to the 13 adverse conditions was also given. This 

specific information was not all completely related to flight control, but often consists of detection or 

modeling technology that could make an ideal input for future flight control schemes. 

In general, there were two distinct methods for implementing adaptive flight control systems. In the 

first method, the system assists the pilot in operating the aircraft in a degraded state by providing 

additional information on the degradation state of the aircraft and then makes calculations relating to 

alternative use of controls or alternative maneuvers. The second type of implementation involves no 

change in the pilot’s method of operation in relation to a nominal operating environment. The adaptive 

control system takes the nominal control inputs given by the pilot, performs calculations based on the 

degradation state of the aircraft, and sends alternative commands to the vehicle control surfaces and/or 

propulsion system. This method is very different than the first, as the pilot is not required to alter his/her 

operation of the plane due to the degradation.

There is much more research being done on failures and degradation of specific components in 

relation to adaptive control systems. Although component research is important, as it can provide better 

input data into adaptive control systems, there is still much more work that can be done to advance the 

state of the art of the actual adaptive control systems. In regards to the two methods of implementing 

adaptive control, the trend is that the state of the art is moving in the direction of systems in which the 

pilot operates the aircraft the same in both nominal and off-nominal conditions where the adaptive control 

system itself alters the commands that go to the control surfaces and propulsion system when operating in 

an off-nominal state.

As the scope of this study would make an indepth discussion of each technology infeasible, readers 

are encouraged to examine the references for more detailed information on the technologies summarized 

in the various sections. It is also acknowledged that only a small selection of current work in the various 

fields could be discussed in the paper. Additional references not cited in the paper that may offer a 

broader perspective of the current technologies are available by contacting the author. 
NASA/TM—2010-216226 17 

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NASA/TM—2010-216226 23 

Appendix A.—Acronyms 

AE acoustic emission

AEPHM Aircraft Electrical Power Systems Prognostics and Health Management

AFCI arc fault circuit interrupting

AFRL Air Force Research Laboratory

AirSTAR Airborne Subscale Transport Aircraft Research

ASIAS Aviation Safety Information Analysis and Sharing

ASRS Aviation Safety Reporting System

AvSAFE Aviation Safety Program

CFD computational fluid dynamics

ECI eddy current inspection

ECU VBV electronic control unit variable bleed valve

EDMS Engine Distress Monitoring System

EMA electromechanical actuator

E-turb enhance turbulence

FAA Federal Aviation Administration

FADEC full authority digital engine control

FBG fiber Bragg grating

FIP forecast icing potential

FOD foreign object damage

GE General Electric

GPS Global Positioning System

IDMS Ingested Debris Monitoring System

IFCS Intelligent Flight Control System

IPA induced positron analysis

IRAC Integrated Resilient Aircraft Control

ITFA integrated turbulence forecasting algorithm

JSF Joint Strike Fighter

JSF PHM Joint Strike Fighter Prognostics and Health Management

NASA National Aeronautics and Space Administration

NCAR National Center for Atmospheric Research

NDI nondestructive inspection

NTSB National Transportation Safety Board

NWS nose wheel steering

PASD pulse-arrested spark discharge

PZT piezoelectric transducers

RAVEN Real-Time Indoor Autonomous Vehicle Test Environment

RLI robust laser interferometer

RUL remaining useful life

SFET seeded fault engine test

SHM structural health monitoring

SWAN stress wave analysis

TAPS Turbulence Autopilot Reporting System

TDR time domain reflectometry

UAV unmanned aerial vehicle
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14. ABSTRACT

Literature from academia, industry, and other Government agencies was surveyed to assess the state of the art in current Integrated Resilient 

Aircraft Control (IRAC) aircraft technologies. Over 100 papers from 25 conferences from the time period 2004 to 2009 were reviewed. An 

assessment of the general state of the art in adaptive flight control is summarized first, followed by an assessment of the state of the art as 

applicable to 13 identified adverse conditions. Specific areas addressed in the general assessment include flight control when compensating 

for damage or reduced performance, retrofit software upgrades to flight controllers, flight control through engine response, and finally test 

and validation of new adaptive controllers. The state-of-the-art assessment applicable to the adverse conditions include technologies not 

specifically related to flight control, but may serve as inputs to a future flight control algorithm. This study illustrates existing gaps and 

opportunities for additional research by the NASA IRAC Project.

15. SUBJECT TERMS

Flight safety; Aircraft safety 

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