Dynamic events-based adaptive NN output feedback control of interconnected nonlinear systems under general output...

Area of Science:

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

Background:

• Interconnected nonlinear systems present challenges in adaptive control due to unmeasurable states and output constraints.
• Existing control algorithms often require continuous signal transmission, leading to high communication burdens.

Purpose of the Study:

• To develop a dynamic event-triggered output constraint control algorithm for adaptive neural network (NN) output feedback tracking control.
• To address the limitations of existing methods by reducing communication frequency and handling flexible constraint boundaries.

Main Methods:

• A reduced-order dynamic gain K-filter was designed to estimate unmeasurable states.
• An asymmetric, time-varying constraint function was proposed to manage potentially unlimited initial constraint boundaries.
• A dynamic event-triggered mechanism utilizing an arctangent function was implemented to minimize control signal transmissions.

Main Results:

• The proposed control algorithm ensures all signals in the closed-loop system remain bounded.
• The tracking error is rigorously proven to satisfy the specified output constraints.
• Numerical simulations validated the effectiveness and performance of the developed control strategy.

Conclusions:

• The novel dynamic event-triggered control algorithm effectively addresses adaptive NN output feedback tracking for interconnected nonlinear systems.
• The proposed method offers a more efficient and robust solution compared to traditional control strategies by reducing data transmission and accommodating dynamic constraints.