Abstract: An air vehicle includes a flight computer, a fuselage, and two rotors mounted symmetrically with respect to the fuselage. Each of the two rotors includes a servo mechanism to tilt a respective rotor of the two rotors about two axes of the respective rotor. The flight computer is configured to send control parameters to each of the two rotors, the control parameters including a rotational speed, a first tilt angle about a first axis of the two axes of the respective rotor, and a second tilt angle about a second axis of the two axes of the respective rotor. A method for operating the air vehicle is also provided.

Abstract: A system and associated methodology determine navigation parameters of a vehicle under varying center of gravity position and varying unknown gravitational forces. The system uses high precision accelerometers arranged in a plurality of configurations.

Abstract: A system and associated methodology determine navigation parameters of a vehicle under varying center of gravity position and varying unknown gravitational forces. The system uses high precision accelerometers arranged in a plurality of configurations.

Abstract: A system and method of controlling quadrotor air vehicles (QRAV) that may include an additional two degrees of freedom for each of the four propellers of the QRAV. Each of the four rotors may be allowed to rotate (tilt) around two local axes selected from the x-axis (roll), y-axis (pitch), and z-axis (yaw). Control of the quadrotor including the additional two degrees of freedom allows thrust of each rotor to be direct in any direction of a semi-sphere. As a result, total control inputs of the QRAV may be increased to twelve, enabling smooth control to achieve superior and precise maneuverability. Additionally, the system and method is fault tolerant and capable of handling failures of any of the rotors. Commands to the propellers may be fully decoupled and achieved independently thereby giving pilots better control to execute difficult maneuvers.

Abstract: The method for optimizing and controlling pressure in gas-oil separation plants utilizes a genetic algorithm-based control method for controlling pressure in each stage of a multi-stage gas-oil separation plant to optimize oil production parameters. A neural network simulation model is used with an optimization procedure to provide on-line operation optimization of the multi-stage gas-oil separation plant. Pressure set points of each stage are automatically and continuously adjusted in the presence of fluctuating ambient temperatures and production rates to ensure optimal oil recovery and optimal quality of the produced oil.