LEARNING-OPTIMAL CONTROL FOR UNMANNED SUPERMANEUVERABLE TECHNOLOGIES (LOCUST)

The development and implementation of nonlinear, learning control systems for Unmanned Air Vehicles (UAVs) will have three major impacts upon their capabilities; increased survivability, extended range of operations, and reduced development costs. The Learning Optimal Control for Unmanned Supermaneuverable Technologies (LOCUST) system will team Neurodyne, Inc. and Sikorsky Aircraft in the utilization of neural network based system identification techniques and optimal control methods to rapidly learn changes in a vehicle's aerodynamic behavior and adapt to sudded meteorological effects while ensuring flight stability. The combined use of neural networks and nonlinear supermaneuverable flight control approaches will also utilize the nonlinear characteristics of the vehicle's dynamical response to increase maneuverability and thus increase survivability. In this effort, Neurodyne and Sikorsky will leverge current research programs in neural networks, nonlinear control, and UAV technology to ensure successful development of LOCUST. The investigators will further leverage in-house and commercially developed on-board sensors and processing techniques to increase both autonomy and range. LOCUST development will also benefit from the investigator's experience in research and development of neural network based reconfigurable flight control systems for the McDonnell F-15 aircraft and RC UAVs to ensure development of a cost-effective, robust adaptive control system.