A neural network baseline problem for control of aircraft flare and touchdown
Neural networks for control
Genetic design of rule-based fuzzy controllers
Genetic design of rule-based fuzzy controllers
Practical Genetic Algorithms with CD-ROM
Practical Genetic Algorithms with CD-ROM
A learning algorithm for continually running fully recurrent neural networks
Neural Computation
| Hi-index | 0.00 |
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.