Feed forward adaptive learning based tracking of spacecraft attitude

In this paper, we propose a design methodology for the tracking of spacecraft attitude. The plant (spacecraft model) is a second order nonlinear multi-input-multi-output system. We stabilize the plant using state feedback. The stability of the closed-loop is assured using a Lyapunov function. Then the step response of the closed-loop system is used to design PID controller using standard classical techniques. Then adaptive feed-forward learning based adaptive filter is used for accomplishment of the tracking objective. The stabilizing controller is independent of the plant parameters. Further, we do not require plant parameters for the tracking. The parameters of adaptive inverse are directly estimated online. The over-all closed-loop gives robust performance as the controller design depends on the output of the plant and not on the parameters of the plant. Computer simulation results are given to illustrate the effectiveness of the proposed controller.