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Practical predefined-time adaptive fuzzy control for quantized nonlinear systems via observer-differentiator scheme.

Yuanqing Wang^1,2, Juan Chen^3,4, Wenyao Ma^5,6

¹Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang, 524088, China.

Scientific Reports

|March 2, 2026

Summary

This study introduces a new fuzzy control method for nonlinear systems with input quantization, ensuring stability in a set time. The approach effectively estimates system states and handles signal fluctuations for reliable performance.

Keywords:

Adaptive fuzzy control Input quantization Predefined-time control State observer

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Area of Science:

Control Systems Engineering
Fuzzy Logic Systems
Nonlinear System Analysis

Background:

Non-strict feedback nonlinear systems (NSFNS) present significant control challenges.
Input quantization introduces non-smoothness and non-differentiability, complicating control design.
Estimating unmeasurable states is crucial for effective output feedback control.

Purpose of the Study:

To develop a predefined-time adaptive fuzzy control strategy for NSFNS with input quantization.
To design a fuzzy logic-based state observer for estimating unmeasurable states.
To propose a predefined-time differentiator to address quantization issues and reduce control signal fluctuations.

Main Methods:

A novel inverse hyperbolic sine state observer utilizing fuzzy logic was designed.
A predefined-time differentiator was developed to handle non-smooth quantized signals.
Theoretical analysis was performed to prove practical predefined-time stability (PPTS).

Main Results:

The observer-based output feedback closed-loop system achieved practical predefined-time stability.
All system signals were demonstrated to be bounded.
The tracking error converged to a small neighborhood of zero within the specified settling time, and observation error was suppressed.

Conclusions:

The proposed adaptive fuzzy control scheme effectively addresses predefined-time control for NSFNS with input quantization.
The integration of a fuzzy state observer and a predefined-time differentiator ensures system stability and performance.
Simulation results validated the practical effectiveness of the developed control strategy.