2 research outputs found

    Finite Impulse Response Filtering Algorithm with Adaptive Horizon Size Selection and Its Applications

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    It is known, that unlike the Kalman filter (KF) finite impulse response (FIR) filters allow to avoid the divergence and unsatisfactory object tracking connected with temporary perturbations and abrupt object changes. The main challenge is to provide the appropriate choice of a sliding window size for them. In this paper, the new finite impulse response (FIR) filtering algorithm with the adaptive horizon size selection is proposed. The algorithm uses the receding horizon optimal (RHOFIR) filter which receives estimates, an abrupt change detector and an adaptive recurrent mechanism for choosing the window size. Monotonicity and asymptotic properties of the estimation error covariance matrix and the RHOFIR filter gain are established. These results form a solid foundation for justifying the principal possibility to tune the filter gain using them and the developed adaptation mechanism. The proposed algorithm (the ARHOFIR filter) allows reducing the impact of disturbances by varying adaptively the sliding window size. The possibility of this follows from the fact that the window size affects the filter characteristics in different ways. The ARHOFIR filter chooses a large horizon size in the absence of abrupt disturbances and a little during the time intervals of their action. Due to this, it has better transient characteristics compared to the KF and RHOFIR filter at intervals where there is temporary uncertainty and may provide the same accuracy of estimates as the KF in their absence. By simulation, it is shown that the ARHOFIR filter is more robust than the KF and RHOFIR filter for the temporarily uncertain systems

    State Estimation Filtering using Recent Finite Measurements and Inputs for Active Suspension System with Temporary Uncertainties

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    In this paper, the finite memory structure(FMS) filter using most recent finite measured outputs and control inputs is applied for the state estimation filtering of automotive suspension systems to verify intrinsic robustness of FMS filter. Firstly, the single-corner model for the automotive suspension system and its state-space model are described. Secondly, FMS as well as infinite memory structure(IMS) filters are briefly introduced and represented by the summation form. Thirdly, a couple of temporary uncertainties, model uncertainty and unknown input, are discussed. Finally, extensive computer simulations are performed for both nominal system and temporarily uncertain system. It is shown that the FMS filter can be better than the IMS filter for both temporary uncertainties. In addition, the FMS filter can be shown to be comparable to the IMS filter after the effects of a couple of temporary uncertainties have completely disappeared
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