Achievability Control Theory for Supervisory Computer-Human Systems

Award Information
Agency: Department of Defense
Branch: Army
Contract: DAAD19-03-C-003
Agency Tracking Number: A022-2735
Amount: $69,579.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: N/A
Solicitation Number: N/A
Timeline
Solicitation Year: N/A
Award Year: 2003
Award Start Date (Proposal Award Date): N/A
Award End Date (Contract End Date): N/A
Small Business Information
1950 Mountain View Road, Lenoir City, TN, 37771
DUNS: N/A
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 John Draper
 President and Chief Scien
 (865) 986-1166
 draperjv@bellsouth.net
Business Contact
 John Draper
Title: President and Chief Scien
Phone: (865) 986-1166
Email: draperjv@bellsouth.net
Research Institution
N/A
Abstract
The objective of the proposed project is the development of technology for more efficient and effective human-computer supervision of complex systems. Systems that combine humans and automation in a synergistic or cooperative manner may be termed hybridsystems. Hybrid systems offer advantages over both purely automated systems and purely manual systems in many circumstances. However, future hybrid systems will be even more complex than contemporary ones. This gives rise to a serious need to developmethods for integrating humans more closely-and more efficiently-than is possible now within hybrid systems. We will achieve this by developing Achievability Control Theory (ACT), an innovative extension of Supervisory Control Theory. The ACT approach haspotential to enhance both the efficiency and flexibility of hybrid systems.During Phase I we will provide a proof of concept by developing the formalized control theory necessary to integrate achievability within a supervisory control framework. Specifically, we will consider the special case when a human participates in a hybridsystem. Successful completion of the proposed research will (1) enhance the flexibility and efficiency of future hybrid systems (including battlefield robots), in turn enhancing the mission success rate, robustness, and survivability; (2) support optimalintegration of humans and computer supervisors in future missions; (3) enhance mission planning for hybrid systems, and (4) guide other research and development by identifying problematic areas within particular missions and by identifying problematicfunctions for hybrid systems generally.

* Information listed above is at the time of submission. *

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