RBF Neural Network-based Guaranteed Cost Nonfragile Control for Spacecraft Electromagnetic Docking

This paper proposes a guaranteed cost nonfragile control approach based on radial basis function (RBF) neural networks to address the high-precision electromagnetic docking problem between a chasing spacecraft and a target spacecraft under parameter uncertainties, control gain perturbations, and lumped disturbances. First, an orbital dynamics model incorporating model parameter uncertainties and control gain perturbations are developed, along with a composite disturbance representation. Then, an RBF neural network-based nonfragile controller is designed to guarantee specified performance in maintaining orbital stability despite multi-source complex disturbances. Finally, numerical simulations of the electromagnetic docking process are performed. Simulation results confirm that the proposed controller enables high-precision electromagnetic docking under multi-source complex disturbances.