Observer-Based Bounded H_∞ Control for Shift-Varying Linear Repetitive Processes With Constrained Bit Rates Over a...

Area of Science:

• Control Systems Engineering
• Networked Systems
• Signal Processing

Background:

• Linear repetitive processes (LRPs) are crucial in applications requiring high-precision, repetitive tasks.
• Controlling LRPs over networks faces challenges due to limited bandwidth and data rate constraints.
• Existing methods often use complex scheduling protocols to manage network resources.

Purpose of the Study:

• To develop an observer-based bounded H-infinity control strategy for shift-varying LRPs over networks with constrained bit rates.
• To optimize bandwidth utilization by introducing a bit rate constraint model and an encoding-decoding mechanism.
• To ensure bounded H-infinity performance within a finite horizon for networked LRPs.

Main Methods:

• Transforming the shift-varying LRP into a shift-varying two-dimensional (2-D) Fornasini-Marchesini model.
• Proposing sufficient conditions in recursive linear matrix inequalities (LMIs) for bounded H-infinity performance.
• Developing a component-based bit rate allocation strategy to expand the quantization region.

Main Results:

• Sufficient conditions derived using LMIs guarantee bounded H-infinity performance for the closed-loop system.
• The proposed encoding-decoding mechanism effectively manages data transmission under bit rate constraints.
• The component-based bit rate allocation strategy enhances quantization capabilities.

Conclusions:

• The developed observer-based bounded H-infinity control is effective for networked LRPs with bit rate constraints.
• The method offers improved bandwidth efficiency compared to traditional scheduling protocols.
• The approach is validated through a simulation example, demonstrating its practical applicability.