Adaptive Saturated Neural Network Tracking Control of Spacecraft: Theory and Experimentation

Abstract

An adaptive saturated neural network (NN) controller is developed for 6 degree-of-freedom (6DOF) spacecraft tracking, and its hardware-in-the-loop experimental validation is tested on the ground-based test facility. To overcome the dynamics uncertainties and prevent the large control saturation caused by the large tracking error at the beginning operation, a saturated radial basis function neural network (RBFNN) is introduced in the controller design, where the approximate error is counteracted by an adaptive continuous robust term. In addition, an auxiliary dynamical system is employed to compensate for the control saturation. It is proved that the ultimate boundedness of the closed-loop system is achieved. Besides, the proposed controller is implemented into a testbed facility to show the final operational reliability via hardware-in-the-loop experiments, where the experimental scenario describes that the simulator is tracking a planar trajectory while synchronizing its attitude with the desired angle. Experimental results illustrate that the proposed controller ensures that the simulator can track a preassigned trajectory with robustness to unknown inertial parameters and disturbances.