  In recent decades, one of the scientists' main concerns has been to improve
the accuracy of satellite attitude, regardless of the expense. The obvious
result is that a large number of control strategies have been used to address
this problem. In this study, an adaptive neuro-fuzzy integrated (ANFIS)
satellite attitude estimation and control system was developed. The controller
is trained with the data provided by an optimal controller. A pulse modulator
is used to generate the right ON/OFF commands of the thruster actuator. To
evaluate the performance of the AN-FIS controller in closed-loop simulation, an
ANFIS observer is used to estimate the attitude and angular velocities of the
satellite using magnetometer, sun sensor and data gyro data. In addition, a new
ANFIS system will be proposed and evaluated that can jointly control and
estimate the system. The performance of the ANFIS controller is compared to the
optimal PID controller in a Monte Carlo simulation with different initial
conditions, disturbance and noise. The results show that the ANFIS controller
can surpass the optimal PID controller in several aspects, including time and
smoothness. In addition, the ANFIS estimator is examined and the results
demonstrate the high ability of this designated observers. Both the control and
estimation phases are simulated by a single ANFIS subsystem, taking into
account the high capacity of ANFIS, and the results of using the ANFIS model
are demonstrated.
