Optimal Real-Time Approach and Capture of Uncontrolled Spacecraft

Abstract

An optimal real-time neural-network-based controller for an on-orbit service mission is proposed. The problem can be mathematically formulated as a complex optimal control problem due to the coupling nature of the orbit, the attitude, and the manipulator of the chaser spacecraft. A hierarchical optimization procedure is developed to efficiently solve the problem by recursively applying three optimization modules. The relative motion characteristics and the decoupled wrist–arm feature are used to transform the solved optimal solutions into easier learnable samples. A series of deep neural network (DNN) controllers are designed to learn from these samples. The performances of the trained network controllers are analyzed by altering the number of learned trajectories and the structure of networks. Simulation results illustrate that the designed DNN controllers can successfully guide a chaser to approach and capture an uncontrolled target with tolerable errors.