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Interactive and Visualized Online Experimentation System for Engineering Education and Research

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Published on: November 24, 2021

A predictive control-based approach to networked Hammerstein systems: design and stability analysis.

Yun-Bo Zhao¹, Guo-Ping Liu, David Rees

¹Faculty of Advanced Technology, University of Glamorgan, CF37 1DL Pontypridd, UK. yzhao@glam.ac.uk

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society

|June 19, 2008

Summary

This summary is machine-generated.

This study introduces a predictive control method for networked Hammerstein systems. The approach addresses nonlinearity and communication issues, ensuring system stability in networked control environments.

Related Experiment Videos

Last Updated: Jul 4, 2026

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Area of Science:

Control Systems Engineering
Networked Systems
Nonlinear Systems

Background:

Hammerstein systems exhibit static input nonlinearity.
Networked control systems face challenges like communication delays and data dropouts.
Ensuring closed-loop stability in such systems is critical.

Purpose of the Study:

To propose a predictive control-based approach for Hammerstein systems in networked environments.
To address the static input nonlinearity inherent in Hammerstein systems.
To compensate for communication constraints including delay and data dropouts.

Main Methods:

A two-step predictive controller is designed to manage the Hammerstein system's nonlinearity.
A delay and dropout compensation scheme is integrated to handle network imperfections.
Theoretical analysis is performed to guarantee closed-loop stability.

Main Results:

The proposed predictive control strategy effectively handles Hammerstein system nonlinearities.
The compensation scheme successfully mitigates the impact of network delays and dropouts.
Theoretical results confirm the closed-loop stability of the controlled networked system.

Conclusions:

The developed predictive control approach is valid for networked Hammerstein systems.
The method provides a robust solution for controlling nonlinear systems over networks.
The findings contribute to the advancement of robust networked control strategies.