Fuzzy H∞ Filter Design for Nonlinear Discrete-Time Systems with Multiple Time-Delays
The problem of robust H ∞ filtering for systems with uncertain external disturbances and measurement noises has been of great interests [7, 18, 21]. The advantage of using an H ∞ filter over a Kalman filter is that no statistical assumption about the noise signals is required. In robust H ∞ filtering, the noise signals are assumed to be arbitrary with bounded energy (or average power). H ∞ filters are designed by minimizing signal estimation errors for bounded disturbances and noises of the worst case. Thus, H ∞ filters are more robust than Kalman filters. Moreover, the H ∞ filtering approach provides both a guaranteed noise attention level and a strong robustness against unmodeled dynamics.
KeywordsIEEE Transaction Fuzzy System Linear Matrix Inequality Fuzzy Model Extended Kalman Filter
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