基于充分调整的RBF神经网络的远程网络控制器设计【外文原文】.pdf

work Controller Design Based on Fully Tuned RBF work

Yun-an Hu, Jing Li, Bin Zuo
Department of Control Engineering, Naval Aeronautical Engineering Institute, Yantai, 264001
Lijing19772006@


Abstract uncertain worked control plant, the paper
adopts the fully tuned RBF NN pensate
Considering a class worked control systems nonlinearities and generalized uncertainties. Then the
(NCS) with generalized uncertainty and nonlinearities, a state feedback control is utilized to control NCS with
control strategy based on fully tuned RBF neural time-varying delay, and the stability of the closed-loop
network(NN) feedback linearization and remote state NCS is effectively guaranteed by Lyapunov stability
feedback control is presented in the paper. Firstly, the theory (Theory 1). Finally, the simulation results show
weight W , center value φ and incidence σ of the fully that this method is very effective.

tuned RBF NN are designed pensate the
nonlinearities and generalized uncertainties. Then the 2. Theoretical foundations
state feedback control is utilized to control NCS with
time-varying delay, and the stability of the closed-loop The adaptive control theory of works will be
NCS is effectively guaranteed by Lyapunov stability utilized to study NCS with nonlinearity and generalized
theory. Finally, the simulation results show that this uncert