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Enhanced Control of Nonlinear Systems Under Control Input Constraints and Faults: A Neural Network-Based Integral

Guangyi Yang¹, Stelios Bekiros², Qijia Yao³

¹Information Center, Hunan Institute of Metrology and Test, Changsha 410014, China.

Entropy (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

This study introduces a novel control method for nonlinear systems, combining neural networks and fuzzy logic to address actuator faults and limitations. The approach ensures finite-time stability and robust performance in real-world applications.

Keywords:

control input constraints faults control finite-time stability fuzzy logic integral sliding surface neural network estimator

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Area of Science:

Control Systems Engineering
Artificial Intelligence in Engineering
Nonlinear System Dynamics

Background:

Existing control techniques often neglect system faults and physical limitations, hindering real-world applicability.
There is a critical need for advanced control strategies that accommodate actuator faults and constraints in practical systems.

Purpose of the Study:

To develop an innovative control approach for nonlinear systems that robustly handles control actuator faults and physical limitations.
To enhance system adaptability and reduce chatter using an intelligent observer with fuzzy logic regulation.

Main Methods:

A neural network-based sliding mode control algorithm integrated with fuzzy logic systems.
An intelligent observer incorporating a fuzzy logic engine to manage actuator faults and limitations.
Finite-time stability analysis to validate the control design.

Main Results:

The proposed controller effectively maintains system regulation despite control input constraints and faults.
The intelligent observer with fuzzy logic reduces system chatter and improves adaptability.
Finite-time convergence and stability of the closed-loop system are demonstrated.

Conclusions:

The developed control strategy offers a robust solution for nonlinear systems facing actuator faults and limitations.
The approach ensures finite-time stability and effective performance in both autonomous and non-autonomous systems.
This research advances the practical implementation of control systems in challenging real-world scenarios.