Turbulence Encountered Landing Control Using Hybrid Intelligent System
During a flight, take-off and landing are the most difficult operations in regard to safety issues. Aircraft pilots must not only be acquainted with the operation of instrument boards but also need flight sensitivity to the ever-changing environment, especially in the landing phase when turbulence is encountered. If the flight conditions are beyond the preset envelope, the automatic landing system (ALS) is disabled and the pilot takes over. An inexperienced pilot may not be able to guide the aircraft to a safe landing at the airport. This paper proposes an intelligent aircraft automatic landing controller that uses recurrent neural network (RNN) controller with genetic algorithm (GA) to improve the performance of conventional ALS and guide the aircraft to a safe landing.
KeywordsPitch Angle Recurrent Neural Network Wind Turbulence Genetic Algorithm Search Hybrid Intelligent System
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