Neural network-based adaptive fault-tolerant control for nonlinear systems with uncertainties.

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear systems present significant challenges for control due to complex dynamics in inputs, states, and outputs.
• Measurement uncertainties and nonlinearities complicate the design of robust control systems.
• Existing fault-tolerant control (FTC) methods often struggle with real-time uncertainty estimation in such environments.

Purpose of the Study:

• To develop a novel fault-tolerant control (FTC) scheme for nonlinear systems that enables real-time uncertainty estimation.
• To address challenges posed by nonlinear dynamics and measurement uncertainties within an output feedback framework.
• To ensure system stability and achieve asymptotic state tracking in the presence of uncertainties.

Main Methods:

• A neural network (NN) descriptor-based observer is proposed to simultaneously estimate system states and sensor uncertainties, capable of handling unbounded uncertainties.
• Neural networks are employed as universal approximators to model the complex nonlinear dynamics of the system.
• A robust model reference tracking controller utilizes the observer's estimates to maintain desired system performance and stability.

Main Results:

• The proposed NN descriptor-based observer effectively estimates system states and sensor uncertainties in nonlinear systems.
• The robust controller guarantees system stability and asymptotic state tracking, even with estimated uncertainties.
• The FTC scheme demonstrates efficacy through theoretical validation and application to real-world case studies.

Conclusions:

• The developed FTC scheme offers a robust solution for real-time uncertainty estimation in challenging nonlinear systems.
• The integration of NN-based observers and robust controllers provides a powerful framework for enhancing system performance and reliability.
• The approach is validated for practical applicability in complex engineering systems.