Neural network Approximation-based Nonlinear Model Predictive Control for Station-keeping of A Multi-vectored Propeller Airship

In this paper, A neural network estimation-based nonlinear predictive model control(NN-NMPC) method is proposed to improve the station-keeping performance for a multi-vectored propeller airship. First, the kinematic model and dynamic model of the airship is introduced, and the station-keeping control problem is formulated. Then, a nonlinear model predictive control strategy, combined with radial basis function neural network(RBFNN) approximation is specified to the station-keeping problem, and the stability analysis is implemented. Finally, the simulation results are illustrated to prove the robustness and effectiveness of the proposed NN-NMPC controller. Simulation results show that NN-NMPC method can attenuate the unknown parameters of the airship effectively and drive it to the desired fixed point.