2018/05/11 by Hamid Khodabandehlou, Khodabandehlou, H., M. Sami Fadali +1
Computer Science · Engineering · #Control Systems and Identification #FOS: Electrical engineering #Neural Networks Stability and Synchronization #Signal Processing (eess.SP) #Stability and Control of Uncertain Systems #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1805.04549
openalex publication_date 2018/05/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.