Adaptive fuzzy hybrid unscented/H-infinity filter for state estimation of nonlinear dynamics problems

Abstract

In this paper, a new hybrid unscented Kalman (UKF) and unscented $H_{\infty}$ (U $H_{\infty}$ F) filter is presented that can adaptively adjust its performance better than that of either UKF and/or U $H_{\infty} F$ , accordingly. In this way, two Takagi-Sugeno-Kang (TSK) fuzzy logic systems are presented to adjust automatically some weights that combine those UK and U $H_{\infty}$ filters, independent of the dynamics of the problem. Such adaptive fuzzy hybrid unscented Kalman/ $H_{\infty}$ filter (AFUK $H_{\infty}$ ) is based on the combination of gain, a priori state estimation, and a priori measurement estimation. The simulation results of an inverted pendulum and a re-entry vehicle tracking problem clearly demonstrate robust and better performance of this new AFUK $H_{\infty}$ filter in comparison with those of both UKF and U $H_{\infty}$ F, appropriately. It is shown that, therefore, the new presented AFUK $H_{\infty}$ filter can simply eliminate the need for either UKF or U $H_{\infty}$ F effectively in the presence of Gaussian and/or non-Gaussian noises.

Keywords

Hybrid Kalman filter adaptive fuzzy Kalman filter nonlinear observer and filter design

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