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Neural Network-Based Fixed-Time Tracking Control for Input-Quantized Nonlinear Systems With Actuator Faults.

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|September 2, 2022

Summary

This summary is machine-generated.

This study presents a fixed-time control strategy for nonlinear systems with quantized inputs and infinite actuator faults. The method ensures tracking errors converge within a pre-assigned time, independent of initial conditions.

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Area of Science:

Control Systems Engineering
Nonlinear Dynamics
Artificial Intelligence

Background:

Strict-feedback nonlinear systems are prevalent in various engineering applications.
Quantized inputs and actuator faults pose significant challenges to system control and stability.
Achieving precise tracking control under these conditions requires robust and adaptive strategies.

Purpose of the Study:

To develop a fixed-time tracking control scheme for strict-feedback nonlinear systems.
To address the complexities introduced by quantized inputs and potentially infinite actuator faults.
To ensure tracking errors converge within a finite, pre-determined time.

Main Methods:

Utilizing Radial Basis Function Neural Networks (RBFNNs) for approximating unknown nonlinear system dynamics.
Designing novel adaptive estimation and auxiliary signals to counteract quantization and fault effects.
Employing fixed-time control theory for guaranteed convergence time.

Main Results:

The proposed control scheme guarantees output tracking error convergence to a small neighborhood of the origin within a fixed time.
The settling time is independent of initial system states and can be pre-assigned.
All signals within the closed-loop system are proven to remain bounded.

Conclusions:

The developed control algorithm effectively handles nonlinear systems with quantized inputs and actuator faults.
Fixed-time convergence is achieved, offering predictable performance bounds.
The approach is validated through numerical and practical single-link manipulator examples.