Universal neural network control of MIMO uncertain nonlinear systems.

Area of Science:

• Control Systems Engineering
• Nonlinear Dynamics
• Artificial Intelligence in Control

Background:

• High-order multi-input-multi-output (MIMO) uncertain nonlinear dynamic systems present significant control challenges.
• External disturbances and unknown varying control direction matrices further complicate achieving precise tracking control.
• Existing control methods may suffer from chattering effects or require high actuator bandwidth.

Purpose of the Study:

• To propose a continuous tracking control law for a class of uncertain nonlinear dynamic systems.
• To address challenges posed by external disturbances and unknown control direction.
• To ensure smooth control signals, relax actuator bandwidth requirements, and avoid chattering.

Main Methods:

• A controller integrating high-gain feedback, a Nussbaum gain matrix selector, an online approximator (OLA) using a two-layer neural network, and a robust term was developed.
• The controller design guarantees the continuity of the control signal.
• Asymptotic tracking performance was theoretically analyzed using standard Lyapunov analysis.

Main Results:

• The proposed continuous tracking control law effectively handles high-order MIMO uncertain nonlinear systems.
• The controller successfully mitigates the effects of external disturbances and unknown control direction.
• The online approximator, implemented as a two-layer neural network, adapts to system uncertainties.
• Asymptotic tracking performance was theoretically proven via Lyapunov stability analysis.
• Simulation results validated the control feasibility and effectiveness.

Conclusions:

• The developed continuous tracking control law offers a robust solution for complex nonlinear systems.
• The controller's continuous nature enhances practical applicability by reducing actuator demands and eliminating chattering.
• The combination of advanced control techniques, including neural networks, provides a powerful framework for addressing system uncertainties and disturbances.