Exponential Position and Attitude Tracking Control of Spacecraft with Unbiased Parameter Identification

This article investigates the position and attitude tracking control problem of a combined spacecraft subject to inertia parametric uncertainties, external disturbance, and input saturation in the postcapture of a noncooperative target. By introducing a group of auxiliary filters, a regression expression with respect to all the unknown parameters, including the mass, the inertia matrix, the center of mass location, and the disturbance-related parameters, is derived without requiring the linear and angular accelerations of the combined spacecraft. On this basis, a sufficient rank-condition of parameter identification is given based on concurrent learning. Subsequently, an adaptive tracking controller is designed for the combined spacecraft by incorporating concurrent learning into the backstepping technique. Within the framework of Lyapunov theory, the proposed controller can simultaneously guarantee the exponential convergence of position and attitude tracking and the unbiased parameter identification of all the uncertain parameters. Finally, numerical simulations are performed to illustrate the effectiveness of the proposed controller.