Neural network-based sliding mode control for satellite attitude tracking

This paper studies the intelligent sliding mode control for satellite dynamics based on neural learning and disturbance observer. We consider the dynamics uncertainty due to uncertain inertia matrix and the disturbance torque caused by orbit transfer engine. A neural network is used to approximate the unknown dynamics, while an adaptive observer is utilized to handle the unknown disturbance. The prediction error is constructed by introducing the estimation model to understand the uncertainties better. Based on the finite-time design, an efficient controller is designed to achieve the attitude tracking. The system stability is guaranteed under the proposed control design while the sliding mode converges in finite time. Better learning and control performances are presented by conducting the simulation studies.