Online Self-Triggered Transmission Control With Critic Learning for Discrete Nonlinear Systems

Area of Science:

• Control Systems Engineering
• Machine Learning
• Nonlinear Dynamics

Background:

• Discrete-time nonlinear systems present challenges in optimal regulation.
• Traditional event-based control methods can incur significant computational burden.
• Ensuring system stability during control is paramount.

Purpose of the Study:

• To develop a novel online self-triggered transmission control (STTC) framework.
• To address the optimal regulation problem in discrete-time nonlinear systems.
• To reduce computational complexity compared to traditional methods.

Main Methods:

• A critic learning technique is employed for optimal control.
• A self-sampling function is designed based on system state for triggering.
• Model, critic, and action networks facilitate online learning.
• Real-time adjustment of control policy to optimal levels.

Main Results:

• The proposed STTC framework ensures system stability.
• Theoretical analysis confirms excellent triggering performance.
• The method effectively reduces computational burden by avoiding continuous judgment.
• Online critic learning allows real-time optimization of control policy.

Conclusions:

• The novel online STTC framework provides an effective solution for optimal regulation of discrete-time nonlinear systems.
• The self-sampling function enhances efficiency and reduces computational load.
• The critic learning approach ensures adaptive and optimal control policy adjustments.