A Hybrid PSO Fuzzy-MRAC Controller Based on EULERINT for Satellite Attitude Control

This paper presents a new hybrid optimal fuzzy controller which is based on the reference model modification of a model reference adaptive controller. The controller is applied to a satellite with uncertainty in the moment of inertia for attitude control in presence of disturbance torques. First, a Mamdani fuzzy logic is used to fuzzify the reference model, then the fuzzy logic membership functions are optimized by a Particle Swarm Optimization (PSO) algorithm. The uncertain parameters estimation of the model, along with the optimal fuzzy reference model provide a robust and inexpensive controller to cope with the uncertainties and disturbances during the satellite maneuver. The PSO method cost function is the integral of control effort plus the EULERINT criterion which the criterion represents the integral of Euler angle around the Euler axis. The cost function has a significant effect on the control effort reduction in addition to maneuver speed increment. This method reduces the EULERINT criterion which indicates a decrease in maneuver as a critical factor in controller design. The numerical simulation represents the better performance of the controller than the conventional MRAC or fuzzy controller.