  Unmanned aerospace vehicles usually carry sensors (i.e., electro-optical
and/or infrared imaging cameras) as their primary payload. These sensors are
used for image processing, target tracking, surveillance, mapping, and
providing high-resolution imagery for environmental surveys. It is crucial to
obtain a steady image in all these applications. This is typically accomplished
by using multi-axis gimbal systems. This paper concentrates on the modeling and
control of a multi-axis gimbal system. A novel and fully outlined procedure is
proposed to derive the nonlinear and highly coupled Equations of Motion of the
two-axis gimbal system. Different from the existing literature, Forward
Dynamics of the two-axis gimbal system is modeled using multi-body dynamics
modeling techniques. In addition to the Forward Dynamics model, the Inverse
Dynamics model is developed to estimate the complementary torques associated
with the state and mechanism-dependent, complex disturbances acting on the
system. A disturbance compensator based on multilayer perceptron (MLP)
structure is implemented to cope with external and internal disturbances and
parameter uncertainties through the torque input channel. Our initial
simulations and experimental work show that the new NN (neural network)-based
controller is performs better in the full operational range without requiring
any tuning or adjustment when compared with well-known controllers such as
cascaded PID, ADRC (Active Disturbance Rejection Control), Inverse Dynamics
based controllers.
