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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Analysis and Design of Launch Vehicle Flight Control Systems

This paper describes the fundamental principles of launch vehicle flight control analysis and design. In particular, the classical concept of "drift-minimum" and "load-minimum" control principles is re-examined and its performance and stability robustness with respect to modeling uncertainties and a gimbal angle constraint is discussed. It is shown that an additional feedback of angle-of-attack or lateral acceleration can significantly improve the overall performance and robustness, especially in the presence of unexpected large wind disturbance. Non-minimum-phase structural filtering of "unstably interacting" bending modes of large flexible launch vehicles is also shown to be effective and robust.

Wie, Bong↗
NASA NTRS2000

Evaluation of X-38 Crew Return Vehicle Input Control Devices in a Microgravity Environment

This report was created by students from Longview High School, Longview, Texas. Longview High School was selected from a group of Texas high schools to participate in the 1999 Texas Fly High Program. This program gives Texas high school students a chance to work with NASA engineers to design and fly a real-world experiment aboard the KC-135 during zero-g parabolas. Jeffrey Fox's role was to provide a concept for the experiment and to mentor the students in its design and testing. The students were responsible for executing all phases of the project. The X-38 Project Office at the Lyndon B. Johnson Space Center Johnson Space is designing a crew return vehicle (CRV) to be docked at the International Space Station for crew rescue in an emergency. Vehicle controls will be almost completely automated, but a few functions will be manually controlled. Four crew input control devices were selected for evaluation by Longview High School students as part of the 1999 Texas Fly High program. These were (1) Logitech Trackman Marble (optical trackball), (2) Smart Cat Touchpad. (3) Microsoft SideWinder 3D-Pro Joystick, and (4) Microsoft SideWinder Gamepad. In two flight tests in the KC-135 aircraft and a series of ground tests, the devices were evaluated for ability to maneuver an on-screen cursor, level of accuracy, ease of handling blind operations, and level of user comfort in microgravity. The tests results led to recommendation of further tests with the Joystick and the Trackman by astronauts and actual space station residents.

Welge, Kirsten↗
NASA NTRS2002

Flexible Wing Base Micro Aerial Vehicles: Assessment of Controllability of Micro Air Vehicles

In the last several years, we have developed unique types of micro air vehicles that utilize flexible structures and extensible covering materials. These MAVs can be operated with maximum dimensions as small as 6 inches and carry reasonable payloads, such as video cameras and transmitters. We recently demonstrated the potential of these vehicles by winning the Fourth International Micro Air Vehicle Competition, held at Ft. Huachucha, Arizona in May 2000. The pilots report that these vehicles have unusually smooth flying characteristics and are relatively easy to fly, both in the standard RC mode and "through the camera" when at greater distances. In comparison, they find that similar sized vehicles with more conventional rigid construction require much more input from the pilot just to maintain control. To make these subjective observations more quantitative, we have devised a system that can conveniently record a complete history of all the RC transmitter stick movements during a flight. Post-flight processing of the stick movement data allows for direct comparisons between different types of MAVs when flown by the same pilot, and also comparisons between pilots. Eventually, practical micro air vehicles will be autonomously controlled, but we feel that the smoothest flying and easiest to fly embodiments will also be the most successful in the long run. Comparisons between several types of micro air vehicles will be presented, along with interpretations of the data.

Jenkins, David A.↗
NASA NTRS1973

Automatic speed control of highway traffic

Vehicle control system monitors all vehicles in its range, and automatically slows down speeding vehicles by activating governor in vehicle. System determines only maximum speed; speeds below maximum are controlled by vehicle operator. Loss of transmitted signal or activation of emergency over-ride will open fuel line and return control to operator.

Klingman, E. E.↗
NASA NTRS2002

Reusable Launch Vehicle Attitude Control Using a Time-Varying Sliding Mode Control Technique

In this paper we present a time-varying sliding mode control (TVSMC) technique for reusable launch vehicle (RLV) attitude control in ascent and entry flight phases. In ascent flight the guidance commands Euler roll, pitch and yaw angles, and in entry flight it commands the aerodynamic angles of bank, attack and sideslip. The controller employs a body rate inner loop and the attitude outer loop, which are separated in time-scale by the singular perturbation principle. The novelty of the TVSMC is that both the sliding surface and the boundary layer dynamics can be varied in real time using the PD-eigenvalue assignment technique. This salient feature is used to cope with control command saturation and integrator windup in the presence of severe disturbance or control effector failure, which enhances the robustness and fault tolerance of the controller. The TV-SMC ascent and descent designs are currently being tested with high fidelity, 6-DOF dispersion simulations. The test results will be presented in the final version of this paper.

Shtessel, Yuri B.↗
NASA NTRS2010

Foot Pedals for Spacecraft Manual Control

Fifty years ago, NASA decided that the cockpit controls in spacecraft should be like the ones in airplanes. But controls based on the stick and rudder may not be best way to manually control a vehicle in space. A different method is based on submersible vehicles controlled with foot pedals. A new pilot can learn the sub's control scheme in minutes and drive it hands-free. We are building a pair of foot pedals for spacecraft control, and will test them in a spacecraft flight simulator.

Love, Stanley G.↗
NASA NTRS1984

Remote control for motor vehicle

A remote controller is disclosed for controlling the throttle, brake and steering mechanism of a conventional motor vehicle, with the remote controller being particularly advantageous for use by severely handicapped individuals. The controller includes a remote manipulator which controls a plurality of actuators through interfacing electronics. The remote manipulator is a two-axis joystick which controls a pair of linear actuators and a rotary actuator, with the actuators being powered by electric motors to effect throttle, brake and steering control of a motor vehicle adapted to include the controller. The controller enables the driver to control the adapted vehicle from anywhere in the vehicle with one hand with minimal control force and range of motion. In addition, even though a conventional vehicle is adapted for use with the remote controller, the vehicle may still be operated in the normal manner.

Johnson, Dale R.↗
NASA NTRS1982

Design, simulation and evaluation of advanced display concepts for the F-16 control configured vehicle

Advanced display concepts to augment the tracking ability of the F-16 Control Configured Vehicle (CCV) were designed, simulated, and evaluated. A fixed-base simulator was modified to represent the F-16 CCV. An isometric sidearm control stick and two-axis CCV thumb button were installed in the cockpit. The forward cockpit CRT was programmed to present an external scene (numbered runway, horizon) and the designed Heads Up Display. The cockpit interior was modified to represent a fighter and the F-16 CCV dynamics and direct lift and side force modes were programmed. Compensatory displays were designed from man-machine considerations. Pilots evaluated the Heads up Display and compensatory displays during simulated descents in the presence of several levels of filtered, zero-mean winds gusts. During a descent from 2500 feet to the runway, the pilots tracked a point on the runway utilizing the basic F-16, F-16 CCV, and F-16 CCV with advanced displays. Substantial tracking improvements resulted utilizing the CCV modes, and the displays were found to even further enhance the tracking ability of the F-16 CCV.

Klein, R. W.↗
NASA NTRS1993

Neighboring suboptimal control for vehicle guidance

The neighboring optimal feedback control law is developed for systems with a piecewise linear control for the case where the optimal control is obtained by nonlinear programming techniques. To develop the control perturbation for a given deviation from the nominal path, the second variation is minimized subject to the constraint that the final conditions be satisfied (neighboring suboptimal control). This process leads to a feedback relationship between the control perturbation and the measured deviation from the nominal state. Neighboring suboptimal control is applied to the lunar launch problem. Two approaches, single optimization and multiple optimization for calculating the gains are used, and the gains are tested in a guidance simulation with a mismatch in the acceleration of gravity. Both approaches give acceptable results, but multiple optimization keeps the perturbed path closer to the nominal path.

Hull, David G.↗
NASA NTRS1993

Neighboring suboptimal control for vehicle guidance

The neighboring optimal feedback control law is developed for systems with a piecewise linear control for the case where the optimal control is obtained by nonlinear programming techniques. To develop the control perturbation for a given deviation from the nominal path, the second variation is minimized subject to the constraint that the final conditions be satisfied (neighboring suboptimal control). This process leads to a feedback relationship between the control perturbation and the measured deviation from the nominal state. Neighboring suboptimal control is applied to the lunar launch problem. Two approaches, single optimization and multiple optimization, for calculating the gains are used, and the gains are tested in a guidance simulation with a mismatch in the acceleration of gravity. Both approaches give acceptable results, but multiple optimization keeps the perturbed path closer to the nominal path.

Hull, David G.↗
NASA NTRS1974

The manual control of vehicles undergoing slow transitions in dynamic characteristics

The manual control was studied of a vehicle with slowly time-varying dynamics to develop analytic and computer techniques necessary for the study of time-varying systems. The human operator is considered as he controls a time-varying plant in which the changes are neither abrupt nor so slow that the time variations are unimportant. An experiment in which pilots controlled the longitudinal mode of a simulated time-varying aircraft is described. The vehicle changed from a pure double integrator to a damped second order system, either instantaneously or smoothly over time intervals of 30, 75, or 120 seconds. The regulator task consisted of trying to null the error term resulting from injected random disturbances with bandwidths of 0.8, 1.4, and 2.0 radians per second. Each of the twelve experimental conditons was replicated ten times. It is shown that the pilot's performance in the time-varying task is essentially equivalent to his performance in stationary tasks which correspond to various points in the transition. A rudimentary model for the pilot-vehicle-regulator is presented.

Moriarty, T. E.↗
NASA NTRS2001

Neural network based automatic limit prediction and avoidance system and method

A method for performance envelope boundary cueing for a vehicle control system comprises the steps of formulating a prediction system for a neural network and training the neural network to predict values of limited parameters as a function of current control positions and current vehicle operating conditions. The method further comprises the steps of applying the neural network to the control system of the vehicle, where the vehicle has capability for measuring current control positions and current vehicle operating conditions. The neural network generates a map of current control positions and vehicle operating conditions versus the limited parameters in a pre-determined vehicle operating condition. The method estimates critical control deflections from the current control positions required to drive the vehicle to a performance envelope boundary. Finally, the method comprises the steps of communicating the critical control deflection to the vehicle control system; and driving the vehicle control system to provide a tactile cue to an operator of the vehicle as the control positions approach the critical control deflections.

Calise, Anthony J.↗