Adaptive Output Feedback Control of Spacecraft Formation Flying Using Chebyshev Neural Networks

Abstract

This paper proposes two adaptive neural controllers, termed adaptive NN controller-I and adaptive NN controller-II, for the output feedback control of spacecraft formation flying in the presence of structured and unstructured uncertainties. The proposed control scheme employs the linear reduced-order observer to generate pseudo–velocity related signals and Chebyshev neural networks to approximate unknown nonlinearities in the system dynamics. The adaptive NN controller-II, which employs a switching function to generate a switch between the robust control, using the smooth hyperbolic tangent function, and the adaptive NN control, can efficiently avoid the overestimation problem (i.e., the bound of the Chebyshev neural network’s output is larger than the corresponding bond of the approximated unknown function) existing in the adaptive NN controller-I. Hence, it requires small control input. The uniform ultimate boundedness of all signals in the closed-loop system is guaranteed by the Lyapunov direct approach. Simulation studies are presented to show the advantages of the proposed approach over the standard adaptive output feedback approach.