Abstract
This paper is concerned with the development of new adaptive nonlinear estimators which incorporate adaptive estimation techniques for system noise statistics with the robust H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> technique. These include Extended H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> Filter (EHF), State Dependent H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> Filter (SDHF) and Unscented H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> Filter (UHF). The new filters are aimed at compensating the nonlinear dynamics as well as the system modeling errors by adaptively estimating the noise statistics and unknown parameters. For comparison purposes, this adaptive technique has also being applied to the Kalman-based filter which include extended Kalman filter (EKF), state dependent Kalman filter (SDKF) and Unscented Kalman filter (UKF). The performance of the proposed estimators is demonstrated using a two-state Van der Pol oscillator as a simulation example.
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