Flight control actuators are the primary equipment of the Automatic Flight Control System (AFCS) that is used to provide short and long term stabilization. Flight control actuators are electrohydraulic actuators that are directly connected to the flight control surface. The hydraulic flow in these actuators is controlled using an Electro-Hydraulic Servo Valve (EHSV) with reference electrical command. Each EHSV has a null bias command to hold the valve in the null position. The null bias command depends on valve hysteresis, temperature, hydraulic pressure, and reference acceleration command. The null bias command and its variation reduce the tracking performance of the flight control actuators. In this article, we proposed a neural network bias compensator to compensate for the EHSV null bias command and improve the tracking performance of the controller. The nonlinear Hammerstein-Wiener model of the actuator was estimated from the test data. Then, a neural network bias compensator was designed in addition to the lead controller. The performance of the neural network bias compensator is analyzed through a series of simulations that demonstrate the desired qualities.

Satcom on the Move (SOTM) antennas are the primary devices for establishing satellite communication in both military and commercial applications. The main design parameters of the SOTM antennas are low cost, low weight, and high data rate. SOTM antennas are basically two or three degrees of freedom robotic manipulators with an antenna payload. In the classical approach, a position and stabilization controller is implemented in order to achieve a high data rate. Most applications use a tracking algorithm to find the maximum RF signal strength by planning a special trajectory for the end effector. In this article, SOTM antennas are modeled and controlled as if they are robotic manipulators. In addition, a neural network controller is implemented to control the robot manipulator and find the maximum RF signal. The neural network controller includes filtered computed torque control (CTM), robustifying signal, and 2 layers neural network structure. The filtered CTM and robustifying signal ensure the closed-loop characteristic, while the neural network structure eliminates nonlinearities and generates the required torque to find the maximum RF signal. The results obtained through a series of simulations demonstrate the desired qualities.