Steering law design for control moment gyros using recurrent neural network

The SGCMG (Single Gimbal Control Moment Gyros) steering law using recurrent neural network is developed. A specific recurrent network was constructed to solve the problem dynamically. With properly chosen parameters, the network could converge to stable states asymptotically, hence the steering error was limited to very small. Without using the pseudoinverse of the Jacobi matrix, the presented method avoids the related calculation problems. Finally, simulation results of a 4-SGCMG system indicate that the proposed algorithm works well.