  The increasing demand for target tracking, environmental surveys,
surveillance and mapping requires multi-axis gimbal systems with high tracking
and stabilization performance. In this paper, first, computed torque model is
generated to estimate the complex disturbances acting on the system. Then, two
different control strategies based on active disturbance rejection control
(ADRC) and computed torque model are implemented on a two-axis gimbal system.
The purpose is to improve the robustness, environmental adaptability and
tracking accuracy of the system and reduce the tuning effort of ADRC by
integrating a neural network (NN) based disturbance compensator (NN assisted
ADRC). In the second control strategy, NN is replaced with a computed torque
model (CTM assisted ADRC), whose inputs come from plant outputs. The simulation
results show that, NN and CTM assisted ADRC structures can decrease mean
tracking errors up to 85.4% and 40.8%, respectively.
