NASA 

National Aeronautics and 

Facts

Space Administration 

Dryden Flight Research Center 

P.O. Box 273

Edwards, California 93523 

Voice 661-276-3449 

FAX 661-276-3566 

pao@dfrc.nasa.gov FS-2002-09-076-DFRC 

Intelligent Flight Control System

Project Summary 

The Intelligent Flight Control System (IFCS) flight research project at NASA Dryden Flight Research 

Center was established to exploit a revolutionary technological breakthrough in aircraft flight controls that can 

efficiently optimize aircraft performance in both normal and failure conditions. IFCS is designed to incorporate 

self-learning neural network concepts into flight control software to enable a pilot to maintain control and safely 

land an aircraft that has suffered a major systems failure or combat damage. 

Major control surface or system failures negate an aircraft flight control system’s design assumptions, 

rendering the predefined fixed control system virtually worthless. The IFCS team has integrated innovative 

neural network technologies with state-of-the-art control algorithms and propulsion control concepts to cor- rectly identify and respond to changes in aircraft stability and control characteristics, and immediately adjust to 

maintain the best possible flight performance during an unexpected failure. The adaptive neural network soft- ware “learns” the new flight characteristics, onboard and in real time, thereby helping the pilot to maintain or 

regain control and prevent a potentially catastrophic aircraft accident. 

The testbed aircraft selected to validate the IFCS neural network flight control software is a highly-modified 

McDonnell-Douglas NF-15B Eagle that was formerly flown in the Advanced Control Technology for Integrated Vehicles 

project at NASA Dryden from 1996 through 1999. 

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The flight test results will be utilized in an overall 

strategy aimed at advancing neural network-based flight 

control technology to new aerospace systems designs, 

specifically targeting the U.S. Air Force C-17 T1 test 

aircraft. This aircraft, based at Edwards Air Force Base, 

Calif., is currently being modified to support IFCS experiments as part of the Intelligent Vehicle Systems (IVS) 

activity. Other vehicle classes will be considered as 

opportunities arise, including fighter and transport aircraft, 

reusable launch vehicles, uninhabited vehicles, and space 

vehicles. 

The IFCS project is representative of the type of flight 

research conducted by NASA to explore new control 

technologies, blending intelligent flight controls with 

adaptive airframe structures to expand aircraft performance 

and capabilities. The project is in line with the Revolutionary Vehicles element of NASA’s Aeronautics Blueprint, 

with a specific goal of developing adaptive and faulttolerant flight control systems leading to unprecedented 

levels of safety and survivability for both civil and military 

aircraft. 

The IFCS project is a collaborative effort among 

NASA Dryden Flight Research Center (DFRC), NASA 

Ames Research Center (ARC), Boeing Phantom Works 

(BPW) (St. Louis, MO), the Institute for Software Research 

(ISR), West Virginia University (WVU) Fairmont, West 

Virginia, and the Georgia Institute of Technology (GIT). 

IFCS Objectives 

• Implement and fly neural network software for flight 

controls 

• Develop verification and validation procedures for 

flight critical checkout of non-deterministic neural net 

software 

• Demonstrate safe in-flight simulated failure recovery 

using learning neural network software with adaptive 

flight controls 

The project is scheduled for flight test beginning in 

2003 (IFCS Generation I) and early 2004 (IFCS Generation II). In addition, initial parameter-identification test 

flights with an IFCS neural network that was pre-trained to 

the F-15’s aerodynamic database were flown in 1999. A 

follow-up series of flights are being flown in the summer of 

2002 to calibrate new instrumentation and air data systems 

and repeat several of the test points flown in the 1999 series 

to reduce risk for the Generation I and II flight research 

phases. 

Project Overview 

The ultimate goal of the IFCS project is to develop and 

flight demonstrate direct adaptive neural network based 

flight control system. The direct adaptive approach incorporates neural networks that are applied directly to the 

flight control system feedback errors to provide adjustments to improve aircraft performance in both normal flight 

and with system failures. A secondary goal is to develop 

the processes of verification and validation (V&V) of 

neural networks for use in flight critical applications. The 

flight project results will be utilized in an overall strategy 

aimed at advancing neural net flight control technology to 

new aerospace system designs including civil and military 

aircraft, reusable launch vehicles, uninhabited vehicles, and 

space vehicles. 

The IFCS is being tested in flight on the NASA NF15B (NASA 837.) This aircraft has been highly modified 

from a standard F-15 configuration to include canard 

control surfaces, thrust vectoring nozzles, and a digital flyby-wire flight control system. The technical approach 

includes: 

• Integrating the Generation I and II online learning 

neural networks with an advanced flight controller; 

• Testing the software on the NASA 837 flight control 

ground test bench; 

• Installation of the system in the aircraft and conducting 

a flight test evaluation in a limited flight envelope at 

NASA Dryden Flight Research Center. The testing will 

include simulated failures to the control system. 

The results of the tests are expected to prove the 

performance and the test process for advanced online 

learning neural network technology. 

IFCS Generation I Concept 

The Intelligent Flight Control System (IFCS) Generation I (GEN I) concept is a direct adaptive neural network 

approach. It is designed to organize and map the aerodynamic changes caused by simulated failure modes, and 

provide the flight control system with that information. The 

system identifies dynamic characteristics of the vehicle 

(stability and control derivatives) and uses that information 

to stabilize the vehicle and provide specific flying characteristics. There are four main components of the GEN I 

system: 

1. A pre-trained neural network (PTNN) 

2. A parameter identification/estimation (PID) algorithm 

3. An online learning neural network, in our case Dy2 
namic Cell Structure (DCS) 

4. A control algorithm, in our case a Stochastic Optimal 

Feedforward Feedback Technique (SOFFT) 

The PTNN is an algorithm that is trained to produce 

stability and control derivatives depending on a specific 

flight condition. It learns the stability and control derivatives from wind tunnel data before flight and is fixed 

throughout the flight. The PTNN provides the first best 

estimate of the vehicle dynamic characteristics. 

The PID algorithm is an online function that determines the actual stability and control characteristics of the 

vehicle as it flies. When results from the PID algorithm 

differ from the PTNN an update to the system is required. 

The DCS provides the online learning of the system. It 

tracks the differences between the PTNN and PID and 

provides an organized map of updates to the stability and 

control derivatives of the vehicle. 

The SOFFT control algorithm uses stability and control 

derivatives information to fine-tune the system to provide 

optimal stability and specific flying characteristics. 

IFCS Generation II Concept 

The Intelligent Flight Control System (IFCS) Generation II (GEN II) neural networks will take over more direct 

control of the vehicle, working alongside the flight controller to adjust for any short-comings. The neural network will 

be more like an equal partner with the flight control system. 

Instead of explicitly identifying how the system has 

changed and then adjusting to compensate, the GEN II 

system is an indirect adaptive system that applies corrections continuously and learns by observing the overall 

effect on performance. Because the GEN II system is more 

closely integrated into the flight controller, it requires 

advances in techniques to flight-qualify learning systems. 

The Generation II concept is based on a dynamic 

inversion controller with a model-following command path. 

The feedback errors are regulated with a proportional plus 

integral (PI) controller. This basic system is augmented 

with an adaptive neural network that operates directly on 

the feedback errors. The adaptive neural network adjusts 

the system for miss-predicted behavior, or changes in 

behavior resulting from damage. Demonstration of this 

direct adaptive neural network is the primary objective of 

the IFCS Generation II flight project. 

The dynamic inversion portion of the control system 

generates acceleration commands. These commands are 

translated into control surface commands by a control 

allocation scheme. The dynamic inversion, control allocation, and model-following algorithm all require information 

on the dynamic behavior of the system to be controlled. 

This information is provided in the form of aerodynamic 

stability and control (S & C) derivatives. The S & C 

derivative calculation algorithm is based on the F-15 IFCS 

Generation I system. 

Flight Phases 

1999 Flight Tests: 

Preliminary flight tests of an IFCS neural network that 

was pre-trained to the NF-15B’s aerodynamic database 

were flown in Spring 1999. These tests, conducted during 

15 flights, validated the non-learning portions of the IFCS 

Generation I concept. As the aircraft flew, the pre-trained 

neural network (PTNN) provided a best estimate of stability and control characteristics based on wind-tunnel information. The SOFFT control algorithm used this information to adjust the control settings to stabilize the system and 

provide specific flying characteristics. These series of flight 

tests demonstrated that: 

• Pre-trained neural networks provided correct online 

stability data for flight control; 

• A flight control aircraft model can be defined from 

online neural networks; 

• Full-time optimal feedback control using neural 

network modeled stability data; 

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• Demonstrated a control system that can provide 

selected handling qualities for any mission mode 

The 1999 tests were flown in a very limited portion of 

the NF-15B’s flight envelope, from about Mach 0.6 to 

Mach 0.9 at 15,000 feeet altitude and from about Mach 

0.85 to Mach 1.4 at 35,000 feet altitude.

2002 Risk Reduction Flights: 

These flights are intended to provide information to 

develop and refine the Parameter Identification/Estimation 

(PID) algorithm. The PID algorithm requires accurate 

measurements of the inputs and the response of the aircraft. 

The NASA NF-15B #837 aircraft instrumentation system 

has been improved to provide better measurements. During 

this flight phase, data will be collected for analysis on the 

ground. This data will be processed by the PID algorithm to 

not only validate the PID algorithm, but to provide a 

representative input to the Dynamic Cell Structure (DCS) 

algorithm. These flight tests will ensure that the PID and 

DCS software will work when it is installed in the aircraft.

 Target goals 

• Functional check flight of aircraft. 

• Checkout of new instrumentation on the aircraft. 

• Calibration for new air data system nose boom; 

• Initial parameter identification (PID) data, using 

newly-installed Airborne Research Test System 

(ARTS) II research flight control computer. 

• Handling quality maneuvers with the pre-trained 

neural network data—completing data set that was 

not completed in the 1999 tests;

 Flight test plan 

• 10 flights over roughly six weeks, with considerable time spent in between each flight for data 

reduction and analysis. 

Generation I Flight Test: 

Flight tests of the Generation I software will occur in 

early 2003. Prior to beginning the Generation I flight tests, 

a faster processor will be added to the NASA NF-15B #837 

aircraft to host the Dynamic Cell Structure (DCS) selflearning portions of the Generation I system. These tests 

will demonstrate the PID and DCS software in a research 

flight control computer during flight. The system will 

demonstrate the ability to compensate for erroneous wind 

tunnel data. In addition, simulated failures will be introduced to show the ability of the system to adapt to changes 

in aircraft behavior. 

Generation II Flight Test: 

Generation II flight tests are presently scheduled for 

early 2004. The performance of this system will be directly 

compared with the performance of Generation I. It is 

expected that the Generation II system will provide quicker 

response to simulated failures. 

For More Information Contact: 

Public Affairs 

NASA Dryden Flight Research Center 

(661) 276-3449 

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