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Related Experiment Video

Updated: May 26, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

Zhongsheng Hou¹, Shangtai Jin

¹Advanced Control Systems Laboratory of the School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China. zhshhou@bjtu.edu.cn

IEEE Transactions on Neural Networks

|December 8, 2011

Summary

This study introduces a data-driven adaptive control method using dynamic linearization for nonlinear systems. The approach ensures system stability and accurate tracking using only measured input/output data.

Related Experiment Videos

Last Updated: May 26, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Control Engineering
Nonlinear System Dynamics
Data-Driven Modeling

Background:

Traditional control methods often require accurate system models, which are difficult to obtain for complex nonlinear systems.
Model-free adaptive control (MFAC) offers an alternative by learning system dynamics from data.
Existing MFAC techniques may have limitations in handling general multiple-input and multiple-output (MIMO) nonlinear discrete-time systems.

Purpose of the Study:

To propose a novel model-free adaptive control (MFAC) approach for MIMO nonlinear discrete-time systems.
To introduce a new dynamic linearization technique (DLT) incorporating pseudo-partial derivatives for controller design.
To demonstrate the effectiveness of the proposed MFAC-DLT approach through analysis and simulations.

Main Methods:

Development of a data-driven model-free adaptive control (MFAC) strategy.
Introduction of a novel dynamic linearization technique (DLT) with pseudo-partial derivatives.
Implementation of compact, partial, and full forms of DLT for controller design.
Validation using extensive simulations on general MIMO nonlinear discrete-time systems.

Main Results:

The proposed MFAC approach, utilizing DLT, successfully controls MIMO nonlinear discrete-time systems.
The controller design relies solely on readily available input/output data, eliminating the need for explicit system models.
Analysis and simulations confirm the achievement of bounded-input bounded-output (BIBO) stability.
Demonstrated convergence of tracking errors, indicating effective system performance.

Conclusions:

The proposed data-driven MFAC approach with DLT provides a robust and effective control solution for complex nonlinear systems.
The method's reliance on measured data simplifies controller design and broadens applicability.
Guaranteed stability and tracking performance highlight the practical utility of this novel control strategy.