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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

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

Tianyou Chai¹, Yajun Zhang, Hong Wang

¹Key Laboratory of Synthetic Automation for Process Industries, Northeastern University, Shenyang 110819, China. tychai@mail.neu.edu.cn

IEEE Transactions on Neural Networks

|November 23, 2011

Summary

This summary is machine-generated.

This study introduces a novel adaptive switching control framework for complex industrial systems. The method uses two controllers to manage nonlinear dynamics, improving stability and performance in challenging control scenarios.

Related Experiment Videos

Last Updated: May 27, 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 Engineering
System Dynamics
Nonlinear Systems

Background:

Complex industrial systems often exhibit multivariable and nonlinear characteristics, making accurate modeling difficult.
Traditional control methods struggle with these systems, especially when model structures are unknown or operating points change.
Existing approaches face limitations in achieving robust control for systems with unmodeled dynamics.

Purpose of the Study:

To propose a new adaptive switching control framework for complex, nonlinear industrial systems.
To address the challenges posed by unknown model structures and unmodeled dynamics.
To enhance both stabilization and performance in system control.

Main Methods:

Development of a controller-driven model framework utilizing input-output data.
Construction of a self-tuning controller based on a linear controller-driven model.
Generation of virtual unmodeled dynamics by comparing controller-driven model outputs with true system outputs.
Design of a second controller based on a nonlinear controller-driven model, incorporating a compensator for virtual unmodeled dynamics.
Integration of the two controllers via an adaptive switching control algorithm.

Main Results:

Analysis of closed-loop system stability and convergence conditions.
Validation of the proposed method through simulations on a coupled nonlinear twin-tank system.
Experimental verification confirming the effectiveness of the adaptive switching control strategy.

Conclusions:

The proposed adaptive switching control framework effectively manages complex, nonlinear industrial systems.
The integration of controller-driven models and virtual unmodeled dynamics offers a robust solution for systems with unknown dynamics.
The method demonstrates superior stabilization and performance compared to traditional approaches, as evidenced by simulation and experimental results.