Adaptive output feedback tracking control for a class of uncertain nonlinear systems using neural networks

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Nonlinear Dynamics

Background:

• Designing robust tracking control for uncertain nonlinear Multiple-Input Multiple-Output (MIMO) second-order systems presents significant challenges.
• Existing methods often require full state information or are computationally intensive.

Discussion:

• An adaptive neural network-based output feedback tracking controller is developed.
• This controller guarantees uniform boundedness of all states and signals, and uniform ultimate boundedness of the tracking error.
• It utilizes only output measurements for feedback, simplifying implementation.

Key Insights:

• The controller's reliance on output feedback and adaptive neural networks offers robustness against system uncertainties.
• Neural network implementation is simplified, depending solely on the desired reference trajectory.
• The proposed scheme demonstrates computational simplicity and ease of implementation.

Outlook:

• This approach can be extended to more complex nonlinear systems and various control objectives.
• Further research could explore real-world applications in robotics and autonomous systems.
• Investigating the controller's performance under different types of system nonlinearities and disturbances is warranted.