Event-triggered control of nonlinear networked control systems via a data-based representation.

Area of Science:

• Control Engineering
• Systems Science
• Computer Science

Background:

• Networked control systems (NCS) face challenges with communication bandwidth and stability.
• Event-triggered control strategies aim to optimize data transmission in NCS.

Purpose of the Study:

• To develop novel event-triggered control schemes for nonlinear NCS.
• To reduce communication load while maintaining system stability.
• To propose both static and dynamic event-triggered mechanisms.

Main Methods:

• A data-based representation using second-order Taylor series expansion for controller design.
• An improved data-dependent static event-triggered mechanism with a relaxing parameter.
• A dynamic event-triggered mechanism for adjustable inter-event intervals.
• Linear matrix inequalities (LMIs) to determine control gains and ensure stability.

Main Results:

• The proposed static event-triggered mechanism effectively reduces communication transmission.
• The dynamic version allows for adjustable inter-event intervals.
• Sufficient conditions derived using LMIs guarantee local stability of the closed-loop systems.
• Simulations validate the effectiveness of the proposed control schemes.

Conclusions:

• The developed data-based event-triggered control strategies are effective for nonlinear NCS.
• The proposed mechanisms offer a trade-off between communication reduction and system performance.
• The LMI-based approach provides a systematic way to design stable controllers for these systems.