Intelligent autolanding controller design using neural networks and fuzzy logic

Designing an intelligent controller for landing phase of a jet transport aircraft in presence of different wind patterns, in order to expand the flight safety envelope has been considered. There are some dangerous conditions like gusts and downbursts, which may occur rarely in service life of aircraft, though aircraft must be tested for these dangerous conditions. Then it is desired to design a controller that not only acts well in usual conditions but also has an acceptable performance in those hazardous conditions. Four different types of controllers have been designed named PID, neuro, hybrid neuro-PID and anfis-PID (adaptive network-based fuzzy inference system) controllers. Simulation results show that the anfis-PID, which its inner loop is PID and outer loop is anfis, satisfies desired conditions in presence of very strong gust. However, the performance of neuro-PID is also acceptable. To evaluate the performance of controllers two level of performance have been defined named level I (desired) and level II (acceptable). Also, in comparison with JFK airport gusts two strong wind patterns named strong and very strong winds have been applied.