Adaptive attitude control of spacecraft without velocity measurements using Chebyshev neural network

This paper proposes an adaptive neural controller for the attitude tracking control of a rigid spacecraft without angular velocity measurements and in the presence of an unknown mass moment of inertia matrix and external disturbances. The modified Rodrigues parameters are employed for the representation of spacecraft attitude. The system uncertainty, which may include unknown mass moment of inertia matrix and external disturbances, is estimated by introducing a Chebyshev neural network. The controller is designed by incorporating a filtering technique to generate a pseudo-velocity tracking error signal from attitude angle measurements into the exiting adaptive neural network control scheme using the Chebyshev neural network. The uniform ultimate boundedness of all signals in the closed-loop system is guaranteed by the Lyapunov approach. The proposed controller is robust not only to structured uncertainty such as unknown mass moment of inertia matrix but also to understructured uncertainty such as external disturbances. Results of the numerical simulations state that the proposed controller is successful in achieving high attitude performance in presence of system parameter uncertainties and external disturbances.