Event-triggered H_∞ filter design for sampled-data systems with quantization

Area of Science:

• Control Systems Engineering
• Signal Processing
• Networked Systems

Background:

• Event-triggered mechanisms offer resource savings over time-triggered systems.
• Quantization in sampled-data systems impacts filter performance.
• H∞ filter design is crucial for robust control.

Purpose of the Study:

• To design an H∞ filter for sampled-data systems incorporating quantization and event-triggered control.
• To develop a novel filtering error model considering quantization effects.
• To reduce network resource utilization through an event-triggered approach.

Main Methods:

• An event-triggered mechanism for data packet release.
• A time interval analysis approach for the sampled-data filtering error model.
• Lyapunov-Krasovskii functional (LKF) and linear matrix inequality (LMI) techniques.

Main Results:

• A new sampled-data filtering error model accounting for quantization.
• Conditions derived using LKF and LMIs for asymptotical stability and H∞ performance.
• Co-design of event-triggered and H∞ filter parameters.

Conclusions:

• The proposed event-triggered H∞ filter design is efficient for sampled-data systems with quantization.
• The approach conserves network resources compared to traditional methods.
• Validated through a mass-spring system example.