Research

Our research is focused on bridging the scientific gaps between traditional computer science topics and aerospace engineering topics, while achieving a high degree of closure between theory and experiment. We focus on machine learning and multi-agent systems, intelligent autonomous control, nonlinear control theory, vision based navigation systems, fault tolerant adaptive control, and cockpit systems and displays. What sets our work apart is a unique systems approach and an ability to seamlessly integrate different disciplines such as dynamics & control, artificial intelligence, and bio-inspiration. Our body of work integrates these disciplines, creating a lasting impact on technical communities from smart materials to General Aviation flight safety to Unmanned Air Systems (UAS) to guidance, navigation & control theory. Our research has been funded by AFOSR, ARO, ONR, AFRL, ARL, AFC, NSF, NASA, FAA, and industry.

Re-entry Vehicles

Control of Forward Reaction Control System (RCS) Jets for Atmospheric Flight Risk-Reduction of Shuttle Orbiter During Entry

Collaborative Effort with
Peter F. Covell, NASA Langley Research Center
Alan Strahan, NASA Johnson Space Center

1 January 2005 – 31 December 2006
Supported by:
NASA Johnson Undergraduate Cooperative Student Program
NASA Johnson Graduate Internship Program
Texas A&M University Flight Simulation Laboratory

During shuttle orbiter entry, failure or degradation of flight control effectiveness or vehicle aerodynamics may result in loss of vehicle control. The loss of the shuttle orbiter Columbia was an example of this, and other failure scenarios include:

Debris impact renders aft Orbital Maneuvering System (OMS) / Reaction Control System (RCS) inoperable, or degrades aero surface control effectiveness.
Wing damage induces an aerodynamic asymmetry greater than the baseline flight control system can handle.
Vehicle damage requires flight at off-nominal attitudes to protect damaged regions from extreme thermal load.

In theory, the forward RCS jets can be used to provide additional torque to maintain yaw control in situations where the aft RCS jets alone are insufficient. The forward RCS jets are not currently used for atmospheric flight control on the shuttle orbiter because the baseline controller was designed to sufficiently handle the present flight/risk envelope without using them. This was largely due to an old aerodynamics “myth” that said the forward RCS jet interactions can be adverse, and thus unsuitable for vehicle control purposes. Although it is true that adverse effects can occur at low angle-of-attack, they are far less likely to occur at higher angle-of-attack, and in August 2004 an aerodynamics Proposal Review Team re-visited the concept and concluded that there were no major aero-mechanic issues that would prohibit use of forward RCS for entry control.

The objective of this research is to design a controller that uses the forward RCS jets to provide additional torque for a damaged vehicle, or a vehicle with damaged control surfaces, or damaged aft jets, to augment the nominal controls during entry. Although the shuttle orbiter retains a fuel reserve of forward RCS jet propellant during entry, the ratio of forward and aft jet activity must be balanced to stay within availability constraints and center of gravity limitations. Use of the aileron and rudder trim limits can help this. A control allocation scheme couple with a fault tolerant Structured Adaptive Model Inverse (SAMI) adaptive controller will be used to detect damage induced torque effects and failed jets.

Specific tasks and research objectives:

Assess value of using forward RCS during entry failure scenarios.
Develop updated aero model by extending current aero database above Mach 4.5 to characterize RCS jet interactions.
Design wrap-on control law to include forward RCS.
Conduct non real-time simulator evaluation.
Conduct preliminary risk assessment.
Conduct real-time simulator evaluation using Shuttle Engineering Simulator (SES).
Implement control allocation scheme.
Synthesize and develop Structured Adaptive Model Inverse (SAMI) adaptive controller.

Potential long-term study elements include investigation of the multi-axis RCS contributions afforded by the forward pitch RCS jets, and the aft pitch and roll jets.

Working with me on this program is Undergraduate Research Assistant:

Carolina Restrepo

Synthesis and Evaluation of Robust Dynamic Inversion Flight Controllers for X-38 Class Re-Entry Vehicles

GN&C Design and Analysis Branch, NASA Johnson Space Center
1 May 2000 – 1 May 2001
Total award $97,320

As opposed to traditional synthesis techniques, in which the nonlinear plant is separated into several linearized models at discrete operating points, and a closed-loop controller is synthesized for each one, Dynamic Inversion seeks to synthesize a global control law from a single nonlinear model. Two open research issues are the “user friendliness” of designing Dynamic Inversion controllers, and controller robustness and fragility.

A previous Dynamic Inversion study on the X-38 conducted at Texas A&M (see below) partially addressed the first of these open issues by generating a comprehensive design guidelines document complete with tutorials, procedures, tools, and examples.

With regard to the second issue, Dynamic Inversion by itself cannot assure stability and performance robustness to disturbances and perturbations in the plant and controller. Therefore, an additional robust control technique must be married to the Dynamic Inversion controller to ensure robustness. There are several robust control techniques and robustness measures currently available to the control designer. Examples in the current literature show a tendency to use whatever robust control and analysis techniques the designer is most familiar with, as opposed to those which are best for a particular application. H-infinity and Mu-synthesis are two of the more popular techniques.

Specific tasks and research objectives:

Demonstrate practical application of the guidelines, procedures, tools, and software previously developed, and validate the design guidelines document. This will be done with a Dynamic Inversion controller design case study for a re-entry vehicle.
Identify and evaluate the advantages that European Dynamic Inversion methods have to offer in terms of ease of use, and suitability for implementation, compared to the particular Dynamic Inversion approach commonly used in North America. These advantages will be directly incorporated into the comprehensive design methodology.
Develop new, non-conservative robustness measures, and examine the fragility of Dynamic Inversion control laws.

Working with me on this program are Graduate Research Assistants:

Jennifer A. Georgie
Dai Ito

Evaluation of Dynamic Inversion as a Flight Control Methodology for Re-entry Vehicles

GN&C Design and Analysis Branch, NASA Johnson Space Center
16 February 1999 – 16 February 2000
Co-P.I. Donald T. Ward
Total award $64,307

Pictured above is the NASA X-38 Crew Return Vehicle (CRV), also known as the Lifeboat in Space. The CRV will provide personnel on the International Space Station with the capability to safely return to Earth in the event of an emergency. As currently designed it will carry seven people, and will be flown autonomously, i.e. no one on board need be a pilot to safely land it.

One of the flight control methodologies which will permit this capability is Dynamic Inversion. Also called Feedback Linearization, it is a non-traditional methodology for synthesizing closed-loop control laws. As opposed to traditional techniques whereby the nonlinear plant is separated into several linearized models at discrete operating points and a closed-loop controller is synthesized for each one, Dynamic Inversion seeks to synthesize a global control law from a single nonlinear model. It has been applied to paper studies of controllers for aircraft such as the F-18 HARV, and has been flown successfully on the X-36.

Specific questions to be answered by this research are:

Is the methodology suitable for a flight vehicle with an extreme range of operating conditions (hypersonic-supersonic-transonic-subsonic) like the X-38?
Is the method suitable for rapid prototyping? Specifically, is software validation of the resulting control laws straightforward and rapid?
For which type of applications and in what circumstances (range of operating conditions or flight regimes) is output feedback suitable as opposed to full-state feedback?
Is it sufficiently robust to handle flight vehicle uncertainties (aerodynamics and mass properties), atmospheric distrurbances, and effector failures?

Working with me on this program is Graduate Research Assistant:

Dai Ito