Incremental learning-based optimal design of BFN kernel for online spacecraft disturbance rejection control

Low earth orbit (LEO) space environment is glutted with unknown and uncertain disturbance, which impede the regular operation and dynamic stability of spacecraft. In recent decades, Basis Function Network (BFN) has attracted much attention benefiting from its potential adaptability for unknown dynamic disturbance. However, it also suffers from the shortcomings of parameter drift, under fitting and long time consuming, which restricts its applications in practical space missions. This paper proposes a new online network reconstruction algorithm, which enables BFN a better excitation effect and more rapid response with less time consumption. The algorithm selects less but more relevant basis functions than routine neural network to approximate the disturbance, which consequently helps to achieve a better characterization effect. Two BFN selection strategies based on iterative learning and batch learning are proposed to further increase the efficiency of online network reconstruction. Detailed theoretical derivation proves the feasibility of the new algorithm. Numerical simulation of a real space station shows that the algorithm has strong adaptability and time efficiency under the interference of structural uncertainty.