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Multi-sensor optimal H∞ fusion filters for delayed nonlinear intelligent systems based on a unified model
Meiqin Liu1, Senlin Zhang, Yaochu Jin
1College of Electrical Engineering, Zhejiang University, Hangzhou 310027, PR China. liumeiqin@zju.edu.cn
Summary
This study introduces optimal H(∞) fusion filtering for nonlinear intelligent systems with time delays. The method ensures stability and reduces noise influence in multi-sensor data fusion for systems like neural networks and fuzzy models.
Area of Science:
- Control Systems Engineering
- Signal Processing
- Artificial Intelligence
Background:
- Nonlinear intelligent systems, including neural networks and Takagi-Sugeno (T-S) fuzzy models, often exhibit time delays.
- Effective data fusion is crucial for accurate state estimation in multi-sensor systems operating under these conditions.
Purpose of the Study:
- To develop a unified approach for designing optimal H(∞) fusion filters for nonlinear intelligent systems with time delays.
- To guarantee the asymptotic stability of the fusion error system and minimize noise impact.
Main Methods:
- A unified system model combining linear dynamics and nonlinear operators is utilized.
- H(∞) performance analysis is conducted using the linear matrix inequality (LMI) approach.
- Centralized and distributed fusion filters are designed, with parameters determined via eigenvalue problem (EVP) solutions.
Main Results:
- The proposed method provides a unified framework for fusion filter design applicable to various nonlinear systems.
- The designed filters ensure the asymptotic stability of the fusion error system.
- Effectiveness is demonstrated through simulation examples, showing reduced noise influence.
Conclusions:
- The developed H(∞) fusion filtering technique offers a robust and unified solution for multi-sensor systems with time delays.
- This approach is broadly applicable to artificial neural networks and fuzzy systems, enhancing their filtering performance.
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