Early warning satellite sliding mode control based on RBF neural networks adaptive learning

The basic structure and mathematics characteristic of RBF (Radius basic function) neural network are studied. In the conditions of the uncertain up bound value can not be measured properly and unknown for early warning satellite dynamic system, adopting RBF neural network adaptive learning the large disturbance up bound value, and reducing the vibration of control and dynamics. For the early warning satellite with motive mirror attitude control problem, a kind of attitude sliding mode control method based neural network disturbance compensation is provided. For RBF network orthogonal least square (OLS) learning algorithm, adopt RBF neural network to learning uncertain up bound value, and design attitude control law of early warning satellite, and solve the dynamics compensation problem of early warning satellite. Mathematic simulation is used to estimate early warning satellite attitude control system guideline based RBF network up bound value adaptive learning sliding mode control.