The Motion Controller Based on Neural Network S-Plane Model for Fixed-Wing UAVs

Aiming at the attitude control problem of fixed wing UAV, this paper introduces S-plane control, which has good control effect in the field of underwater UAV, into the attitude control of UAV. At the same time, aiming at the problem that the coefficient setting of parameters in S-plane control completely depends on experience and cannot be adjusted adaptively, the radial basis function neural network (RBFNN) is introduced, and a neural network S-plane control model which can realize on-line adaptive adjustment of the coefficient of parameters in S-plane control is proposed. The simulation results based on the data of a certain UAV show that compared with the S-plane control, the proposed neural network S-plane control model has the characteristics of fast response speed, strong anti-interference ability, and strong robustness. In addition, it also has the function of adaptive adjustment, which shows good control performance.

Automated summary

This paper introduces the S-plane control from the field of underwater UAV into the attitude control of fixed wing UAV, and proposes a neural network S-plane control model that can realize on-line adaptive adjustment of parameter coefficients. The simulation results show that the proposed model has fast response speed, strong anti-interference ability, and robustness, with the function of adaptive adjustment, demonstrating good control performance.