Prescribed performance tracking control for spacecraft proximity operations with inertia property identification

This paper investigates the integrated position and attitude tracking control with prescribed performance and inertia property identification for spacecraft proximity operations subject to unknown inertia properties, external disturbances, and input saturation. To pursue this, the radical basis function neural network is utilized to approximate the external disturbances, where the involved weight matrix, together with the unknown inertia properties, is considered as parameter uncertainty. Then, a rank condition to guarantee the parameter identification is proposed within the framework of concurrent learning. Subsequently, an integrated position and attitude tracking controller is designed based on dynamic surface method in conjunction with certain performance functions and a saturation compensator, and concurrent learning adaptive laws are developed for parameter update. It is proven that the proposed controller can achieve position and attitude tracking with prescribed performance and, meanwhile, guarantee the uniform ultimate boundedness of all the closed-loop states. Additionally, the inertia properties, including the mass, inertia matrix, and center of mass location, can be identified with high accuracy. Finally, simulation experiments are carried out to evaluate the effectiveness of the proposed control scheme.