High Performance Intelligent Controller for Systems with Unknown Dynamics

Award Information
Agency: Department of Defense
Branch: Defense Advanced Research Projects Agency
Contract: W31P4Q-09-C-0368
Agency Tracking Number: 08SB2-0934
Amount: $98,999.00
Phase: Phase I
Program: SBIR
Awards Year: 2009
Solicitation Year: 2008
Solicitation Topic Code: SB082-062
Solicitation Number: 2008.2
Small Business Information
13619 Valley Oak Circle, Rockville, MD, 20850
DUNS: 620282256
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: Y
Principal Investigator
 Chiman Kwan
 Chief Technology Officer
 (240) 505-2641
Business Contact
 Chihwa Yung
Title: President
Phone: (301) 315-2322
Email: chihwa.yung@signalpro.net
Research Institution
The first generation of intelligent control applications was for systems with unknown dynamics, with on-line learning of the neural network weights. The second generation of intelligent control applications has generally provided off-line design methods that first solve the optimal design equations, and then apply the resulting control to the system with no on-line learning. The system dynamics must generally be known. We propose a novel, high performance, and third generation intelligent control framework, which is applicable to systems with unknown dynamics, including air vehicles, Unmanned Air Vehicles (UAVs), Micro-Air Vehicles (MAVs), robots, motors, and power systems. The framework consists of two cooperative on-line learning modules. First, an adaptive critic network is used to approximate the cost function (performance index) of the system. Second, another action network uses the critic information to adjust certain control parameters in the action network. Major advantages include: 1) system dynamics can be unknown; 2) controller achieves optimal performance in the steady-state; 3) the overall system performance is robust to disturbances and unknown changes in the system dynamics.

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

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