Moving Mass Control For Stratospheric Airship

Stratospheric airships are lighter-than-air vehicles, working at the altitude of 20km in the lower calm part of stratosphere. They can be used as real-time surveillance platforms or space transfer platforms, and have significant applications in future environment monitor, emergence rescue, and civil communication area, etc.Typical actuators of airships are aerodynamic control surfaces, vectored thrust and ballonet. Due to the special working conditions of low density and low speed, these actuators have several deficiencies in attitude control of stratospheric airship. Thus new type actuators and relevant controllers are necessary to develop as a complement.Moving mass control method is proposed for stratospheric airship in this paper. Firstly, the scheme of moving mass actuator is designed and the dynamic equation of airship is derived by using Newton-Euler method.Then based on simulation of dynamic model, the mechanism and influencing factors of attitude control by moving masses are studied. The passive gliding of airship is realized and control capabilities between moving mass actuator and elevator are compared. Finally, the PD combined with feedforward method is designed for pitch control, and two methods including the method based on fuzzy logic controller and fuzzy optimization method are employed to allocate the control moment. Simulation results of the two methods are shown and compared.Research results show that: the motion of masses changes the gravity center of airship, so the moment of inertia and the gravity moment are varied. Meanwhile, the aerodynamic angles are generated, which result in the change of aerodynamic moment acted on the airship. Control efficiency of moving masses is independent of airspeed, so moving mass control has an advantage over the aerodynamic surfaces at low airspeed. The two allocation methods both succeed in the allocation of the control moment. However the method based on fuzzy logic controller consumes less time, while the fuzzy optimization method achieves the least energy-consuming.