Composite neural learning based appointed-time safe approach control under full-state constraints

Abstract

This article investigates composite learning based appointed-time control strategy for spacecraft rendezvous and docking subject to model uncertainties, input saturation, and actuator faults. A novel prescribed performance function (PPF) is designed to constraint all system states and ensure the property of controller insensitive to initial conditions. Combining the artificial potential function (APF) method with transformed error states, a six-degree-of-freedom (6-DOF) coupled relative motion model with full-state and motion constraints is proposed. To obtain higher control accuracy and address the problem of complex calculations in network weight update, a low-complexity composite learning based neural network (NN) weight update law is investigated. The appointed-time controller is designed by utilizing backstepping method, and the dynamic surface technique is used to solve the “dimension explosion” problem. The proposed controller guarantees the stability of the system states in appointed time without the information of target. Three simulation scenarios are carried out to validate the effectiveness and robustness of the proposed controller.