Nonlinear Momentum Transfer Control of a Gyrostat with a Discrete Damper by Using Neural Networks

An adaptive feedback linearization technique combined with neural networks is addressed for the momentum transfer control of a torque-free gyrostat with an attached spring-mass-dashpot damper. The input normalization neural network is used to adaptively compensate for the model error uncertainties of a nutation damper as an internal dynamics and avoid the unnecessary assumptions for stability analysis. The whole spacecraft angular momentum component of the wheel spin axis is selected as the output function of the feedback linearization. Thus, a desired output function is predefined that the total angular momentum of the spacecraft is absorbed into the wheel spin direction at the steady state so that the nutation angle converges to zero. The ultimate boundedness of the tracking error is simply proved by the Lyapunov stability theory. We also investigate the effect of rotor misalignment on the steady spin of the spacecraft. The effectiveness of the proposed control law is verified through simulation results