Networked model predictive control using a wavelet neural network

This study proposes using a wavelet neural network (WNN) with a feedforward component and a model predictive controller (MPC) for online nonlinear system identification and control over a communication network. The WNN performs the online identification of the nonlinear system. The 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. A computationally efficient formulation for the controller is presented to reduce the computational complexity of the MPC for online implementation and Lyapunov theory is used to prove the stability of the MPC. The methodology is applied to the online identification and control of an unmanned autonomous vehicle. Simulation results show that the MPC with an extended prediction horizon can effectively control the system in the presence of fixed or random network delay. © 2020 John Wiley & Sons Ltd