远程操控飞行器自适应神经网络观测器设计

Aiming at the problem that it is difficult to establish the model of the control augmentation system of a remotely piloted vehicle (RPV) accurately due to the nonlinear dynamics of the RPV and the uncertainties of the performance of the RPV control augmentation system, an adaptive neural network state observer is proposed to approximate the model of the RPV control augmentation system. The closed-loop system composed of the RPV dynamics and control augmentation system is taken as a whole, and the nonlinear model of the whole system is established. To deal with the unmodeled dynamics, a neural network algorithm is proposed to identify the nonlinear dynamics model online, and a robust term is induced to suppress the disturbance. Meanwhile, to guarantee the stability of the overall observer system, an adaptive law is designed to turn the neural network weights. Moreover, the overall adaptive observer scheme is proved to be uniformly and ultimately bounded. The simulation results show the effectiveness of the adaptive neural network observer in the presence of the unmodeled dynamics and external disturbance.