  In this study, we use a wavelet neural network with a feedforward component
and a model predictive controller for online nonlinear system identification
over a communication network. The wavelet neural network (WNN) performs the
online identification of the nonlinear system. The model predictive controller
(MPC) uses the model to predict the future outputs of the system over an
extended prediction horizon and calculates the optimal future inputs by
minimizing a controller cost function. The Lyapunov theory is used to prove the
stability of the MPC. We apply the methodology to the online identification and
control of an unmanned autonomous vehicle. Simulation results show that the MPC
with extended prediction horizon can effectively control the system in the
presence of fixed or random network delay.
