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