Comparison of Neural, Fuzzy, Evolutionary, and Adaptive Approaches for Carrier Landing

摘要

This paper compares in simulation six control approaches for an automated carrier landing design problem. The key requirements of this problem are that the aircraft must remain within tight bounds on a three dimensional flight path while approaching the ship, and then touch down in a relatively small area with acceptable sink rate, angular attitudes and speed. Further, this must be accomplished with limited control authority for varying conditions of ship motion, air turbulence, radar tracking noise/data delays, and ship air wake. The control law approaches examined are: fuzzy logic, two neural network approaches, indirect adaptive and non-adaptive versions of dynamic inversion, and a hybrid approach that combines direct and indirect adaptive elements. In some of the cases, a genetic algorithm was used to optimize fixed parameters during design. The approaches were demonstrated on a 6 Degree-of-Freedom simulation with nonlinear aerodynamic and engine models, actuator models with position and rate saturations, and turbulence. Simulation results include statistics for landing with damage to both control and lifting surfaces in different environmental conditions.