Dynamic Event-Based Control for Stochastic Optimal Regulation of Nonlinear Networked Control Systems

Area of Science:

• Control Systems Engineering
• Artificial Intelligence
• Networked Systems

Background:

• Nonlinear systems with communication networks present control challenges.
• Event-triggered control strategies aim to reduce communication load.
• Adaptive Dynamic Programming (ADP) offers a powerful framework for optimal control.

Purpose of the Study:

• To investigate a dynamic event-triggered stochastic adaptive dynamic programming (ADP) approach for nonlinear systems.
• To develop a novel control strategy that enhances stability and efficiency in networked systems.
• To address the limitations of static event-triggered rules by proposing a dynamic approach.

Main Methods:

• Established a novel condition for stochastic input-to-state stability (SISS) in discrete systems.
• Devised an event-triggered control strategy using identifier-actor-critic neural networks (NNs).
• Designed an adaptive static event sampling condition using Lyapunov techniques and introduced a dynamic event-triggered rule.

Main Results:

• Ensured ultimate boundedness (UB) for the closed-loop system.
• Proved that the proposed dynamic event-triggered control strategy avoids the triviality phenomenon by ensuring a sampling interval greater than one.
• Demonstrated the effectiveness of the proposed near-optimal control pattern through simulations.

Conclusions:

• The dynamic event-triggered stochastic ADP approach provides a stable and efficient control solution for nonlinear networked systems.
• The proposed method overcomes the limitations of static event-triggered rules, ensuring practical applicability.
• The research contributes a novel control strategy with proven stability and performance.