View source for Intelligent flight control system

[[File:NASA NF-15B 837 ECANS.jpg|thumb|upright=1.14|NASA's NF-15B was used for the project.]]

The '''Intelligent Flight Control System''' ('''IFCS''') is a next-generation [[flight control system]] designed to provide increased safety for the crew and passengers of [[aircraft]] as well as to optimize the aircraft performance under normal conditions.{{ref|NASA}} The main benefit of this system is that it will allow a pilot to control an aircraft even under failure conditions that would normally cause it to crash. The IFCS is being developed under the direction of [[NASA]]'s [[Armstrong Flight Research Center|Dryden Flight Research Center]] with the collaboration of the NASA [[Ames Research Center]], [[Boeing Phantom Works]], the Institute for Scientific Research at West Virginia University, and the [[Georgia Institute of Technology]].

== Objectives of IFCS ==
The main purpose of the IFCS project is to create a system for use in civilian and military [[aircraft]] that is both adaptive and [[fault tolerant]].{{ref label|NASA|1|a}} This is accomplished through the use of upgrades to the flight control software that incorporate self-learning [[neural network]] technology. The goals of the IFCS neural network project are.{{ref|FlightTest}}
# To develop a [[flight control system]] that can identify aircraft characteristics through the use of neural network technology in order to optimize aircraft performance.
# To develop a neural network that can train itself to analyze the flight properties of the aircraft.
# To be able to demonstrate the aforementioned properties on a modified [[McDonnell Douglas F-15 STOL/MTD|F-15 ACTIVE]] aircraft during flight, which is the [[testbed]] for the IFCS project.

== Theory of operation ==
The [[neural network]] of the IFCS learns flight characteristics in real time through the [[aircraft]]’s [[sensors]] and from error corrections from the [[Flight control computer|primary flight computer]], and then uses this information to create different flight characteristic models for the aircraft{{ref|IFCS}}. The neural network only learns when the aircraft is in a stable flight condition, and will discard any characteristics that would cause the aircraft to go into a failure condition. If the aircraft's condition changes from stable to failure, for example, if one of the [[Flight control surfaces|control surfaces]] becomes damaged and unresponsive, the IFCS can detect this fault and switch the flight characteristic model for the aircraft. The neural network then works to drive the error between the reference model and the actual aircraft state to zero.

== Project history ==

=== Generation 1 ===
Generation 1 IFCS flight tests were conducted in 2003 to test the outputs of the neural network.{{ref label|NASA|1|b}} In this phase, the [[neural network]] was pre-trained using flight characteristics obtained for the [[McDonnell Douglas F-15 STOL/MTD]] in a [[wind tunnel]] test and did not actually provide any control adjustments in flight.{{ref label|FlightTest|2|a}} The outputs of the neural network were run directly to instrumentation for data collection purposes only.

=== Generation 2 ===
Generation 2 IFCS tests were conducted in 2005 and used a fully integrated [[neural network]] as described in the theory of operation.{{ref label|IFCS|3|a}} It is a direct adaptive system that continuously provides error corrections and then measures the effects of these corrections in order to learn new flight models or adjust existing ones.{{ref label|NASA|1|c}} To measure the aircraft state, the neural network takes 31 inputs from the roll, pitch, and yaw axes and the [[Flight control surfaces|control surfaces]].{{ref label|IFCS|3|b}} If there is a difference between the aircraft state and model, the neural network adjusts the outputs of the primary flight computer through a dynamic inversion controller to bring the difference to zero before they are sent to the actuator control electronics which move the control surfaces.

== Intelligent autopilot system ==
A different research and development project with the goal of designing an intelligent flight control system is being carried out at University College London. Their prototype is known as the Intelligent Autopilot System which has Artificial Neural Networks capable of learning from human teachers by imitation. The system is capable of handling severe weather conditions and flight emergencies such as engine failure or fire, emergency landing, and performing Rejected Take Off (RTO) in a flight simulator.{{ref label|IAS|4|a}}

== See also ==
{{Portal|Aviation}}
* [[Aircraft flight control system]]
* [[McDonnell Douglas F-15 Eagle|F-15]]

== References ==
# {{note|NASA}}{{note label|NASA|1|a}}{{note label|NASA|1|b}}{{note label|NASA|1|c}}{{cite web
| title=NASA Dryden Flight Research Center Fact Sheets: Intelligent Flight Control System
| url=http://www.nasa.gov/centers/dryden/news/FactSheets/FS-076-DFRC.html
| publisher=NASA Dryden Flight Research Center
| date=July 21, 2006
| accessdate=2007-02-25
| archive-date=2010-03-24
| archive-url=https://web.archive.org/web/20100324185530/http://www.nasa.gov/centers/dryden/news/FactSheets/FS-076-DFRC.html
| url-status=dead
}}
# {{note|FlightTest}}{{note label|FlightTest|2|a}}{{cite news
| title=Flight test of an intelligent flight-control system
| work=
| url=http://findarticles.com/p/articles/mi_qa3957/is_200310/ai_n9342664
| publisher=Associated Business Publications
|date=October 2003
| accessdate=2007-02-25
| first=Ron
| last=Davidson
}}
# {{note|IFCS}}{{note label|IFCS|3|a}}{{note label|IFCS|3|b}}{{cite web 
| author=Peggy S. Williams-Hayes
| title=Flight Test Implementation of a Second Generation Intelligent Flight Control System
| publisher=NASA Dryden Flight Research Center 
| date= August 25, 2005 
| url=https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20050238105.pdf
}}
# {{note|IAS}}{{note label|IAS|4|a}}{{cite web 
| title=The Intelligent Autopilot System IAS
| url=http://www0.cs.ucl.ac.uk/staff/h.baomar/
| publisher=Haitham Baomar 
| date= August 15, 2016
| accessdate=2016-09-05
}}

[[Category:Flight control systems]]