Remote intelligent control of nonlinear uncertain system under network environment

A remote intelligent control strategy for nonlinear system with uncertainty under network environment is presented, which is based on neural networks nonlinear compensator and linear state feedback controller. A neural networks on-line observer is used to learning the nonlinear characteristics of the local system in order to realize feedback linearization. The weights of neural networks are updated on-line without need of persistency of excitation and the uniformly ultimately bounded stability of the closed-loop error system can be obtained. Finally, a remote controller with state feedback is applied to the linearized system with time-delay, and stability of the closed-loop networked-control system is guaranteed by Lyapunov stability theory.