NEURAL NETWORKS APPROACH TO AIAA AIRCRAFT CONTROL DESIGN CHALLENGE

This paper focuses on designing a discrete-time lateral-directional control law for a high-performance aircraft using neural networks. The control law structure is composed of feedback and filter components formulated in the form of a three layer feedforward neural network whose parameters are adjusted by a gradient descent algorithm to provide stabilization about the aircraft center of mass and asymptotic tracking of pilot command input. The number of parameters was chosen in an ad hoc manner. Only rate gyro and lateral accelerometer outputs are available for feedback, whereas rudder pedal and lateral stick commands are input signals to the filter. Linear simulation results at an operating point within the aircraft's envelope in the presence of atmospheric turbulence and actuator and sensor noises shed light on the ability of neural networks to serve as a practical tool for flight control law designers.