Robust mixed l(1)/H(∞) filtering for affine fuzzy systems with measurement errors.
IEEE Transactions on Cybernetics
|September 10, 2013
Summary
This study introduces a new fuzzy filter design for nonlinear systems with uncertainties. The method improves performance and reduces computational load for robust filtering applications.
Area of Science:
- Control Systems Engineering
- Fuzzy Logic Systems
- Nonlinear System Analysis
Background:
- Robust filtering is crucial for nonlinear systems with uncertainties.
- Existing methods face challenges with measurement errors and computational complexity.
- Fuzzy models are effective for representing nonlinear system dynamics.
Purpose of the Study:
- To develop a robust filtering method for nonlinear affine fuzzy systems with norm-bounded uncertainties.
- To address challenges posed by measurement errors between plant outputs and filter inputs.
- To improve H∞ performance and reduce computational burden compared to existing techniques.
Main Methods:
- Utilizing system outputs as premise variables for fuzzy models and filters.
- Employing a piecewise Lyapunov function combined with the S-procedure.
- Incorporating slack matrix variables within linear matrix inequality (LMI) formulations.
- Designing a fuzzy-basis-dependent mixed l1/H∞ filter.
Main Results:
- A novel fuzzy-basis-dependent filter design is proposed.
- The method effectively reduces worst-case peak output caused by measurement errors.
- The filter satisfies an H∞-norm constraint, ensuring bounded disturbance rejection.
- Demonstrated superior H∞ performance and reduced computational cost via a numerical example.
Conclusions:
- The proposed fuzzy-basis-dependent filter offers enhanced H∞ performance for uncertain nonlinear systems.
- The LMI-based approach provides a systematic and computationally efficient design methodology.
- The method effectively handles measurement errors, improving filter robustness and reliability.
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