A Novel Adaptive Unscented Kalman Filter Attitude Estimation and Control System for a 3U Nanosatellite

A novel adaptive unscented Kalman filter (AUKF) based estimation algorithm is proposed for a 3U Cubsat. This small satellite employs a three axis magnetometer and three MEMS gyroscopes as well as three magnetic torque rods and one reaction wheel on the pitch axis. Unlike the existing UKF, in this paper, an n+1 sigma set is used to estimate the nanosatellite attitude instead of 2n + 1 sigma points as in a conventional UKF. Numerical Simulation results validate the performance of the proposed adaptive Kalman filter. There is no need for linearization of the nonlinear dynamics of the system. The estimated result tracks satellite attitude during the damping and stable control stages. Euler angles, gyro bias, and angular velocity of the satellite are estimated using this proposed AUKF with good convergence time and estimation accuracy.