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Related Experiment Video

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Adaptive neural network prescribed performance control for dual switching nonlinear time-delay system.

Qianqian Mu^1,2, Fei Long^3,4, Bin Li⁵

¹College of Big Data and Information Engineering, Guizhou University, Guiyang, 550025, Guizhou, China. xtqqian@163.com.

Scientific Reports

|May 19, 2023

Summary

This study introduces adaptive neural networks (NNs) for controlling nonlinear systems with time delays. The proposed method ensures bounded system signals and meets prescribed performance tracking constraints.

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Area of Science:

Control Theory
Nonlinear Systems
Artificial Intelligence

Background:

Time-delay systems present significant control challenges.
Neural networks (NNs) offer powerful function approximation capabilities.
Prescribed performance control (PPC) addresses performance degradation in practical applications.

Purpose of the Study:

To develop an adaptive neural network output feedback control scheme.
To address the control problem for dual switching nonlinear systems with time-delay.
To ensure tracking performance meets predefined constraints.

Main Methods:

Utilizing neural network approximations for adaptive control.
Combining prescribed performance control (PPC) with the backstepping method.
Designing a switching rule for system stability.

Main Results:

All signals in the closed-loop system are demonstrated to be bounded.
The designed controller achieves the desired tracking performance.
The tracking performance satisfies the prescribed performance constraints.

Conclusions:

The proposed adaptive NNs output feedback control scheme is effective for dual switching nonlinear time-delay systems.
The integration of PPC and backstepping ensures robust tracking performance.
The controller design successfully handles performance degradation issues.