Search NASA⌕ Search

SEARCH · Search NASA

Results for “flight controls”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Reinforcement Learning Approach to Flight Control Allocation with Distributed Electric Propulsion

The flight control system of the SUSAN Electrofan concept aircraft achieves attitude control using both conventional flight control surfaces and differential thrust through distributed electric propulsion (DEP) from sixteen wing-mounted electric engines. The introduction of eight pairs of wing fans for attitude control creates a highly actuated system. Such a system requires more sophisticated control to operate, especially in the presence of wingfan failures where the loss of a single wingfan can result in a thrust imbalance. This paper investigates the use of deep reinforcement learning (RL) using proximal policy optimization (PPO) to achieve attitude control through a combination of DEP and control surface deflections. First, the paper examines the aircraft undergoing a coordinated turn. Then, it examines the aircraft experiencing a wingfan failure during cruise conditions. It is shown that deep reinforcement learning can be a potential avenue for nonlinear flight control design.

Distributed Electric Propulsion↗
NASA NTRS1989

A knowledge-based system design/information tool for aircraft flight control systems

Flight test experiences of the X-29 forward-swept wing, the advanced fighter technology integration (AFTI) F-16, and the highly maneuverable aircraft technology (HiMAT) programs are reviewed. Significant operating anomalies in these programs and the design errors which caused them are examined. The functions which a system design/information tool should provide to assist designers in avoiding errors are identified.

Mackall, Dale A.↗
NASA NTRS1993

Aircraft digital flight control technical review

The Aircraft Digital Flight Control Technical Review was initiated by two pilot induced oscillation (PIO) incidents in the spring and summer of 1992. Maj. Gen. Franklin (PEO) wondered why the Air Force development process for digital flight control systems was not preventing PIO problems. Consequently, a technical review team was formed to examine the development process and determine why PIO problems continued to occur. The team was also to identify the 'best practices' used in the various programs. The charter of the team was to focus on the PIO problem, assess the current development process, and document the 'best practices.' The team reviewed all major USAF aircraft programs with digital flight controls, specifically, the F-15E, F-16C/D, F-22, F-111, C-17, and B-2. The team interviewed contractor, System Program Office (SPO), and Combined Test Force (CTF) personnel on these programs. The team also went to NAS Patuxent River to interview USN personnel about the F/A-18 program. The team also reviewed experimental USAF and NASA systems with digital flight control systems: X-29, X-31, F-15 STOL and Maneuver Technology Demonstrator (SMTD), and the Variable In-Flight Stability Test Aircraft (VISTA). The team also discussed the problem with other experts in the field including Ralph Smith and personnel from Calspan. The major conclusions and recommendations from the review are presented.

Davenport, Otha B.↗
NASA NTRS2003

Neural Flight Control System

The Neural Flight Control System (NFCS) was developed to address the need for control systems that can be produced and tested at lower cost, easily adapted to prototype vehicles and for flight systems that can accommodate damaged control surfaces or changes to aircraft stability and control characteristics resulting from failures or accidents. NFCS utilizes on a neural network-based flight control algorithm which automatically compensates for a broad spectrum of unanticipated damage or failures of an aircraft in flight. Pilot stick and rudder pedal inputs are fed into a reference model which produces pitch, roll and yaw rate commands. The reference model frequencies and gains can be set to provide handling quality characteristics suitable for the aircraft of interest. The rate commands are used in conjunction with estimates of the aircraft s stability and control (S&C) derivatives by a simplified Dynamic Inverse controller to produce virtual elevator, aileron and rudder commands. These virtual surface deflection commands are optimally distributed across the aircraft s available control surfaces using linear programming theory. Sensor data is compared with the reference model rate commands to produce an error signal. A Proportional/Integral (PI) error controller "winds up" on the error signal and adds an augmented command to the reference model output with the effect of zeroing the error signal. In order to provide more consistent handling qualities for the pilot, neural networks learn the behavior of the error controller and add in the augmented command before the integrator winds up. In the case of damage sufficient to affect the handling qualities of the aircraft, an Adaptive Critic is utilized to reduce the reference model frequencies and gains to stay within a flyable envelope of the aircraft.

Gundy-Burlet, Karen↗
NASA NTRS1996

Development and Flight Evaluation of an Emergency Digital Flight Control System Using Only Engine Thrust on an F-15 Airplane

A propulsion-controlled aircraft (PCA) system for emergency flight control of aircraft with no flight controls was developed and flight tested on an F-15 aircraft at the NASA Dryden Flight Research Center. The airplane has been flown in a throttles-only manual mode and with an augmented system called PCA in which pilot thumbwheel commands and aircraft feedback parameters were used to drive the throttles. Results from a 36-flight evaluation showed that the PCA system can be used to safety land an airplane that has suffered a major flight control system failure. The PCA system was used to recover from a severe upset condition, descend, and land. Guest pilots have also evaluated the PCA system. This paper describes the principles of throttles-only flight control; a history of loss-of-control accidents; a description of the F-15 aircraft; the PCA system operation, simulation, and flight testing; and the pilot comments.

Burcham, Frank W., Jr.↗
NASA NTRS1988

Flight control systems development and flight test experience with the HiMAT research vehicles

Two highly maneuverable aircraft technology (HiMAT) remotely piloted vehicles were flown a total of 26 flights. These subscale vehicles were of advanced aerodynamic configuration with advanced technology concepts such as composite and metallic structures, digital integrated propulsion control, and ground (primary) and airborne (backup) relaxed static stability, digital fly-by-wire control systems. Extensive systems development, checkout, and flight qualification were required to conduct the flight test program. The design maneuver goal was to achieve a sustained 8-g turn at Mach 0.9 at an altitude of 25,000 feet. This goal was achieved, along with the acquisition of high-quality flight data at subsonic and supersonic Mach numbers. Control systems were modified in a variety of ways using the flight-determined aerodynamic characteristics. The HiMAT program was successfully completed with approximately 11 hours of total flight time.

Kempel, Robert W.↗
NASA NTRS2004

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Williams-Hayes, Peggy S.↗
NASA NTRS2005

Flight Test Implementation of a Second Generation Intelligent Flight Control System

The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team was to develop and flight-test control systems that use neural network technology, to optimize the performance of the aircraft under nominal conditions, and to stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. The Intelligent Flight Control System team is currently in the process of implementing a second generation control scheme, collectively known as Generation 2 or Gen 2, for flight testing on the NASA F-15 aircraft. This report describes the Gen 2 system as implemented by the team for flight test evaluation. Simulation results are shown which describe the experiment to be performed in flight and highlight the ways in which the Gen 2 system meets the defined objectives.

Williams-Hayes, Peggy S.↗
NASA NTRS2004

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

The NASA F-15 Intelligent Flight Control System project team has developed a series of flight control concepts designed to demonstrate the benefits of a neural network-based adaptive controller. The objective of the team is to develop and flight-test control systems that use neural network technology to optimize the performance of the aircraft under nominal conditions as well as stabilize the aircraft under failure conditions. Failure conditions include locked or failed control surfaces as well as unforeseen damage that might occur to the aircraft in flight. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to the baseline aerodynamic derivatives in flight. This set of open-loop flight tests was performed in preparation for a future phase of flights in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed a pitch frequency sweep and an automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. An examination of flight data shows that addition of the flight-identified aerodynamic derivative increments into the simulation improved the pitch handling qualities of the aircraft.

Williams, Peggy S.↗
NASA NTRS1998

Using Engine Thrust for Emergency Flight Control: MD-11 and B-747 Results

With modern digital control systems, using engine thrust for emergency flight control to supplement or replace failed aircraft normal flight controls has become a practical consideration. The NASA Dryden Flight Research Center has developed a propulsion-controlled aircraft (PCA) system in which computer-controlled engine thrust provides emergency flight control. An F-15 and an MD-11 airplane have been landed without using any flight control surfaces. Preliminary studies have also been conducted that show that engines on only one wing can provide some flight control capability if the lateral center of gravity can be shifted toward the side of the airplane that has the operating engine(s). Simulator tests of several airplanes with no flight control surfaces operating and all engines out on the left wing have all shown positive control capability within the available range of lateral center-of-gravity offset. Propulsion-controlled aircraft systems that can operate without modifications to engine control systems, thus allowing PCA technology to be installed on less capable airplanes or at low cost, are also desirable. Further studies have examined simplified 'PCA Lite' and 'PCA Ultralite' concepts in which thrust control is provided by existing systems such as auto-throttles or a combination of existing systems and manual pilot control.

Burcham, Frank W., Jr.↗
NASA NTRS1991

The NASA Lewis integrated propulsion and flight control simulator

A new flight simulation facility was developed at NASA-Lewis. The purpose of this flight simulator is to allow integrated propulsion control and flight control algorithm development and evaluation in real time. As a preliminary check of the simulator facility capabilities and correct integration of its components, the control design and physics models for a short take-off and vertical landing fighter aircraft model were shown, with their associated system integration and architecture, pilot vehicle interfaces, and display symbology. The initial testing and evaluation results show that this fixed based flight simulator can provide real time feedback and display of both airframe and propulsion variables for validation of integrated flight and propulsion control systems. Additionally, through the use of this flight simulator, various control design methodologies and cockpit mechanizations can be tested and evaluated in a real time environment.

Bright, Michelle M.↗
NASA NTRS1988

Control Of Flexible Structures-2 (COFS-2) flight control, structure and gimbal system interaction study

The second Control Of Flexible Structures Flight Experiment (COFS-2) includes a long mast as in the first flight experiment, but with the Langley 15-m hoop column antenna attached via a gimbal system to the top of the mast. The mast is to be mounted in the Space Shuttle cargo bay. The servo-driven gimbal system could be used to point the antenna relative to the mast. The dynamic interaction of the Shuttle Orbiter/COFS-2 system with the Orbiter on-orbit Flight Control System (FCS) and the gimbal pointing control system has been studied using analysis and simulation. The Orbiter pointing requirements have been assessed for their impact on allowable free drift time for COFS experiments. Three fixed antenna configurations were investigated. Also simulated was Orbiter attitude control behavior with active vernier jets during antenna slewing. The effect of experiment mast dampers was included. Control system stability and performance and loads on various portions of the COFS-2 structure were investigated. The study indicates possible undesirable interaction between the Orbiter FCS and the flexible, articulated COFS-2 mast/antenna system, even when restricted to vernier reaction jets.

Fay, Stanley↗
NASA NTRS1975

Space Shuttle flight control system

The Space Shuttle is a control stabilized vehicle with control provided by an all digital, fly-by-wire flight control system. This paper gives a description of the several modes of flight control which correspond to the Shuttle mission phases. These modes are ascent flight control (including open loop first stage steering, the use of four computers operating in parallel and inertial guidance sensors), on-orbit flight control (with a discussion of reaction control, phase plane switching logic, jet selection logic, state estimator logic and OMS thrust vector control), entry flight control and TAEM (terminal area energy management to landing). Also discussed are redundancy management and backup flight control.

Klinar, W. J.↗
NASA NTRS2006

X-43A Flight Controls

A viewgraph presentation detailing X-43A Flight controls at NASA Dryden Flight Research Center is shown. The topics include: 1) NASA Dryden, Overview and current and recent flight test programs; 2) Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) Program, Program Overview and Platform Precision Autopilot; and 3) Hyper-X Program, Program Overview, X-43A Flight Controls and Flight Results.

Baumann, Ethan↗