NASA SBIR 2015 Solicitation


PROPOSAL NUMBER: 15-1 A2.02-9001
SUBTOPIC TITLE: Unmanned Aircraft Systems Technology
PROPOSAL TITLE: Verification and Validation of Adaptive Learning Control System Towards Safety Assurance and Trusted Autonomy

SMALL BUSINESS CONCERN (Firm Name, Mail Address, City/State/Zip, Phone)
Intelligent Automation, Inc.
15400 Calhoun Drive, Suite 190
Rockville, MD 20855 - 2737
(301) 294-5221

PRINCIPAL INVESTIGATOR/PROJECT MANAGER (Name, E-mail, Mail Address, City/State/Zip, Phone)
Dr. Devendra Tolani
15400 Calhoun Drive, Suite 190
Rockville, MD 20855 - 2737
(301) 294-4630

CORPORATE/BUSINESS OFFICIAL (Name, E-mail, Mail Address, City/State/Zip, Phone)
Mr. Mark James
15400 Calhoun Drive, Suite 190
Rockville, MD 20855 - 2737
(301) 294-5221

Estimated Technology Readiness Level (TRL) at beginning and end of contract:
Begin: 3
End: 4

Technology Available (TAV) Subtopics
Unmanned Aircraft Systems Technology is a Technology Available (TAV) subtopic that includes NASA Intellectual Property (IP). Do you plan to use the NASA IP under the award?

TECHNICAL ABSTRACT (Limit 2000 characters, approximately 200 words)
In order to fulfill the present and future aerospace needs of the nation, there has been a growing interest in adaptive systems incorporating learning algorithms. Before such adaptive systems can be adopted for use in safety-critical aerospace applications, they must be certified to meet specified reliability and safety requirements. Intelligent Automation Inc. (IAI) in collaboration with Wright State University (WSU) proposes to develop a novel systematic verification and validation framework for adaptive learning flight control systems towards real-time safety assurance and trusted autonomy. A Neural Network (NN) based adaptive controller is designed as an add-on to a previously certified baseline linear controller to enhance robustness to modeling uncertainty and fault-tolerance to system faults. Based on Lyapunov stability theory, an integrity monitoring scheme for the adaptive controller will be developed to detect potential controller malfunctions and unstable learning conditions caused by unanticipated hazardous conditions. The proposed architecture can potentially maximize the use of advanced adaptive controller with high performance capabilities, while ensuring the safety of the overall flight control system in the presence of unanticipated hazards. In Phase I, the algorithms will be demonstrated using a real-time quadrotor test environment.

POTENTIAL NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
There are many potential NASA applications for this innovation, for instance, intelligent adaptive flight control systems, adaptive engine control, space exploration applications including mated flight vehicle coordination, docking, and control of autonomous robots, flyers, and satellites. The national Research Council has identified intelligent and adaptive systems as one of the five common threads for the "51 high-priority R&T challenge". Adaptive systems technologies have been identified explicitly to be the key enabler for intelligent flight controls, advanced guidance and adaptive air traffic management systems for improving safety and maintenance. Successful experimental results developed by NASA researchers have suggested the significant potential of intelligent adaptive control systems. These systems must be certified before they can be adopted for use in safety-critical aerospace applications. Conventional V&V methods are for not suitable for adaptive learning systems, and rigorous novel V&V methods must be developed before intelligent adaptive systems become part of the future.

POTENTIAL NON-NASA COMMERCIAL APPLICATIONS (Limit 1500 characters, approximately 150 words)
The proposed approach can potentially be used for many safety critical applications, including military and commercial aircraft, U.S. air transportation systems, unmanned aerial vehicles, autonomous robots, nuclear power plants, etc. It will lead to benefits in the form of improved safety, survivability, and superior control performance of safety-critical systems.

TECHNOLOGY TAXONOMY MAPPING (NASA's technology taxonomy has been developed by the SBIR-STTR program to disseminate awareness of proposed and awarded R/R&D in the agency. It is a listing of over 100 technologies, sorted into broad categories, of interest to NASA.)
Air Transportation & Safety
Algorithms/Control Software & Systems (see also Autonomous Systems)
Autonomous Control (see also Control & Monitoring)
Command & Control
Condition Monitoring (see also Sensors)
Recovery (see also Autonomous Systems)
Recovery (see also Vehicle Health Management)
Verification/Validation Tools

Form Generated on 04-23-15 15:37