The three-axis active attitude control method with a momentum wheel and magnetic coils for a pico-satellite is considered. The designed satellite is a 2.5 kg class satellite stabilized to nadir pointing. The momentum wheel performs a pitch-axis momentum bias, nominally spinning at a particular rate. Three magnetic coils are mounted perpendicularly along the body axis for precise attitude control through the switch control mechanism. Momentum wheel start up control, damping control and attitude acquisition control are considered. Simulation results show that the proposed combined control laws for the pico-satellite is reliable and has an appropriate accuracy under different separation conditions. The proposed strategy to start up the wheel after separation from the launch vehicle shows that its pitch momentum wheel can start up successfully to its nominal speed from rest, and the attitude convergence can be completed within several orbits, depending on separation conditions.
Satellite attitude information is essential for pico-satellite applications requiring light-weight,low-power,and fast-computation characteristics.The objective of this study is to provide a magnetometer-only attitude estimation method for a low-altitude Earth orbit,bias momentum pico-satellite.Based on two assumptions,the spacecraft spherical symmetry and damping of body rates,a linear kinematics model of a bias momentum satellite's pitch axis is derived,and the linear estimation algorithm is developed.The algorithm combines the linear Kalman filter(KF) with the classic three-axis attitude determination method(TRIAD).KF is used to estimate satellite's pitch axis orientation,while TRIAD is used to obtain information concerning the satellite's three-axis attitude.Simulation tests confirmed that the algorithm is suited to the time-varying model errors resulting from both assumptions.The estimate result keeps tracking satellite attitude motion during all damping,stable,and free rotating control stages.Compared with nonlinear algorithms,such as extended Kalman filer(EKF) and square root unscented Kalman filer(SRUKF),the algorithm presented here has an almost equal performance in terms of convergence time and estimation accuracy,while the consumption of computing resources is much lower.
