Remote Network Controller Design Based on Fully Tuned RBF Neural Network

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