Event-Triggered Practical Prescribed Time Output Feedback Neuroadaptive Tracking Control Under Saturated Actuation

Area of Science:

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

Background:

• Uncertain nonlinear systems pose significant control challenges.
• Actuator saturation and unmeasurable states complicate control design.
• Time-varying control coefficients add further complexity.

Purpose of the Study:

• To develop an event-triggered practical prescribed-time tracking control scheme.
• To address actuator saturation and unmeasurable states in uncertain nonlinear systems.
• To achieve tracking error convergence within a predefined time.

Main Methods:

• A simple neural network-based state observer estimates unmeasurable states.
• An event-triggered output feedback control scheme is designed using error mapping.
• An auxiliary system handles input saturation effects without explicit bounds.

Main Results:

• Successfully estimates system states despite time-varying coefficients.
• Achieves prescribed-time tracking error convergence to a residual set.
• Avoids complex finite-time observers and fractional power feedback.

Conclusions:

• The proposed event-triggered control is less complex and more practical.
• The approach effectively manages actuator saturation and unmeasurable states.
• Offers a viable solution for precise tracking control in challenging systems.