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Stability and Guaranteed Cost Analysis of Time-Triggered Boolean Networks
IEEE Transactions on Neural Networks and Learning Systems
|September 8, 2017
Summary
This study ensures global stability for time-triggered Boolean networks (BNs) using matrix semitensor products and average dwell-time switching. It also presents a guaranteed cost bound for these complex systems.
Area of Science:
- Control Theory
- Networked Systems
- Computational Biology
Background:
- Boolean networks (BNs) are crucial models for simulating biological pathways and complex systems.
- Time-triggered systems introduce complexities in stability analysis due to discrete event dynamics.
- Guaranteed cost control is essential for ensuring system performance under uncertainty.
Purpose of the Study:
- To investigate the stability and guaranteed cost of time-triggered Boolean networks (BNs).
- To develop a method for designing average dwell-time switching signals for enhanced network stability.
- To establish bounds for the infinite time cost function based on stability results.
Main Methods:
- Utilizing the semitensor product of matrices for analyzing Boolean networks.
- Implementing mode-dependent average dwell-time switching signals for time triggering.
- Employing copositive Lyapunov functions to derive stability conditions.
- Formulating and analyzing an infinite time cost function.
Main Results:
- A sufficient condition for global stability of time-triggered BNs is derived.
- The designed average dwell-time switching signal guarantees network stability.
- A bound for the infinite time cost function is successfully presented.
- Numerical examples validate the theoretical findings.
Conclusions:
- The semitensor product approach effectively addresses stability and guaranteed cost in time-triggered BNs.
- Average dwell-time switching is a viable strategy for stabilizing these networks.
- The derived conditions and cost bounds offer practical insights for system design.
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