Data processing: vehicles – navigation – and relative location – Vehicle control – guidance – operation – or indication – Aeronautical vehicle
Reexamination Certificate
2003-05-30
2004-06-15
Black, Thomas G (Department: 3663)
Data processing: vehicles, navigation, and relative location
Vehicle control, guidance, operation, or indication
Aeronautical vehicle
C244S003210, C244S164000, C244S171000, C342S029000, C340S967000
Reexamination Certificate
active
06751529
ABSTRACT:
BACKGROUND
1. Field
The present invention generally relates to aircraft control techniques and, in particular, to a system and method for controlling an aircraft via the use of a neural network controller.
2. Description of the Related Art
Aircraft generally have three ranges or axes of motion (roll, pitch, and yaw), and it is necessary to actively control the aircraft's motion about each of the three axes of motion via one or more aerodynamic actuators. In general, for fixed-wing aircraft (e.g., airplanes), roll, pitch, and yaw are primarily controlled via the aircraft's ailerons, horizontal stabilizer, and vertical stabilizer, respectively. For rotary-wing aircraft (e.g., helicopters), roll and pitch are generally controlled via the aircraft's main or horizontal rotor, and yaw is generally controlled via the aircraft's tail or vertical rotor. However, it is common for a particular actuator to contribute to more than one axis of motion, and it is possible for other types of actuators to be employed in addition to and/or in lieu of the aforementioned actuators.
Properly controlling an aircraft's motion can be a difficult task, particularly in environmental conditions (e.g., turbulence) that cause the aircraft to behave in an unpredictable manner. Indeed, most pilots spend an enormous amount of time and effort in learning how to properly control their aircraft.
Control of model aircraft (i.e., miniature, unmanned aircraft) adds an additional layer of difficulty since there is no on-board pilot that can apply the appropriate inputs for properly controlling the aircraft. A “pilot on the ground” cannot sense nuances in the aircraft movement and, thus, can become disoriented very quickly. For example, if a helicopter is facing away from a pilot (i.e., helicopter nose points in same direction as pilot's nose), then the pilot's left is the helicopter's left. But, if the helicopter yaws 180 degrees and faces the pilot, then the pilot has to change his/her orientation and method of thinking because “left is right” and “right is left.” A pilot on board will never face this problem.
Rotary-wing model aircraft are inherently unstable in that they lack positive dynamic stability. With fixed-wing aircraft, their actuators can sometimes be positioned or configured such that the fixed-wing aircraft generally maintains stable flight without additional input from the actuators (also called trimmed flight). However, most rotary-wing aircraft fly in an unstable manner unless control inputs for the actuators are continuously provided. One drawback is the resulting difficulty of controlling a remote-controlled (RC) aircraft in flight.
For example, in order for a user to successfully fly and control a RC helicopter either for fun or business, the user has to be an expert pilot. In addition to having to know how to fly, the user also needs to know how to autorotate the RC helicopter in the event that the RC helicopter engine quits or stalls in mid air. The skills required to autorotate a helicopter is very different from the skills required to fly the helicopter. Even for RC fixed-wing aircraft, the user needs to know how to glide the aircraft to the ground.
SUMMARY
In one embodiment, a method for controlling an aircraft comprises providing an attitude error as a first input into a neural controller, the attitude error calculated from a commanded attitude and a current measured attitude, providing an attitude rate as a second input into a neural controller, the attitude rate derived from the current measured attitude, processing the first input and the second input to generate a commanded servo actuator rate as an output of the neural controller, generating a commanded actuator position from the commanded servo actuator rate and a current servo position, and inputting the commanded actuator position to a servo motor configured to drive an attitude actuator to the commanded actuator position, wherein, the neural controller is developed from a neural network, the neural network designed without using conventional control laws, the neural network trained to eliminate the attitude error.
In another embodiment, an apparatus for controlling an aircraft comprises an attitude sensor operable to provide a current attitude, a differentiator operable to receive as input the current attitude and derive an attitude rate, a neural controller operable to receive a plurality of inputs comprising an attitude error and the attitude rate, the attitude error calculated from a commanded attitude and the current attitude, the neural controller also operable to generate a commanded servo rate from the plurality of inputs, the commanded servo rate applied to a current actuator position to generate a commanded actuator position, and a servo motor operable receive the commanded actuator position, the servo motor further operable to drive an attitude actuator to the commanded actuator position, wherein the neural controller is developed from a neural network designed without using conventional control laws.
These and other embodiments of the present invention will also become readily apparent to those skilled in the art from the following detailed description of the embodiments having reference to the attached figures, the invention not being limited to any particular embodiment(s) disclosed.
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Black Thomas G
Lanier Ford Shaver & Payne P.C.
Neural Robotics, Inc.
To Tuan C
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