Adaptive output feedback control of uncertain nonlinear systems using single-hidden-layer neural networks

Area of Science:

• Control Theory
• Nonlinear Systems
• Adaptive Control

Background:

• Designing controllers for uncertain nonlinear systems with unknown dynamics and dimensions is challenging.
• Traditional methods often rely on state observers, which are difficult to design for such systems.
• The relative degree of the regulated output is a known parameter.

Purpose of the Study:

• To develop an adaptive output feedback control strategy for uncertain nonlinear systems.
• To address the challenge of unknown system dynamics and dimensions.
• To ensure accurate tracking of a reference trajectory with bounded errors.

Main Methods:

• Designing a controller that focuses on observing the output tracking error, rather than the full system state.
• Utilizing Lyapunov's direct method to prove the ultimate boundedness of error signals.
• Applying the developed control strategy to a fourth-order nonlinear system and a helicopter attitude control system.

Main Results:

• The proposed method successfully achieves bounded error tracking for the uncertain nonlinear system.
• The observer for the output tracking error proved sufficient, simplifying the control design.
• Demonstrated practical applicability through simulations on a complex fourth-order system and a helicopter model.

Conclusions:

• Adaptive output feedback control is feasible for uncertain nonlinear systems even with unknown dimensions.
• Observing the output tracking error is an effective alternative to full state observers.
• The method provides a robust approach for trajectory tracking in complex dynamic systems.