Adaptive neural network tracking control with disturbance attenuation for multiple-input nonlinear systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Nonlinear dynamical systems with unknown nonlinearities present significant control challenges.
• External disturbances (L(2) or L(infinity)) can degrade system performance and stability.
• Existing adaptive control methods may suffer from issues like division by zero or chattering.

Purpose of the Study:

• To design a switching adaptive neural network controller for multiple-input nonlinear affine dynamical systems.
• To achieve arbitrary attenuation of external disturbances.
• To guarantee uniform ultimate boundedness of the tracking error and uniform boundedness of all closed-loop signals.

Main Methods:

• Development of a switching adaptive control strategy utilizing neural networks.
• Implementation of techniques to avoid division by zero and ensure continuity of switching.
• Analysis of system stability and error bounds.

Main Results:

• The proposed controller successfully attenuates L(2) or L(infinity) external disturbances.
• Uniform ultimate boundedness of the tracking error is achieved in the absence of disturbances.
• Uniform boundedness of all closed-loop signals is guaranteed.
• The controller avoids division by zero and alleviates chattering phenomena.

Conclusions:

• The designed switching adaptive neural network controller is effective for nonlinear systems with unknown dynamics.
• The approach provides robust disturbance attenuation and guaranteed stability properties.
• The method overcomes limitations of existing controllers, offering improved performance and reliability.