Networked Model Predictive Control Using a Wavelet Neural Network

TL;DR

This paper presents a networked model predictive control framework utilizing a wavelet neural network for online nonlinear system identification and control, demonstrating stability and effectiveness in autonomous vehicle simulations with network delays.

Contribution

It introduces a novel combination of wavelet neural networks with model predictive control for online nonlinear system identification over networks, with stability proof.

Findings

Effective control of autonomous vehicle in network delay conditions

Stable MPC with extended prediction horizon proven via Lyapunov theory

Simulation results confirm robustness to fixed and random delays

Abstract

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.