Modified Kalman filter-based state estimation for sandwich systems with backlash
LI Yan-yan
TAN Yong-hong
DONG Rui-li
LI Hai-fen
Abstract：Many practical systems in control engineering can be described as the so-called sandwich systems with backlash. As the embedded backlash is a complicated non-smooth nonlinear function with local memory and multi-valued mapping, the estimation of internal states for the whole sandwich systems becomes a challenge. Based on the separation principle for key terms, a non-smooth pseudo-linear state space model for the whole sandwich systems with backlash disturbed by random noises is built by introducing several embedded switch functions to handle the effect of backlash. Then, a non-smooth modified Kalman filtering (MKF) method is proposed to achieve the state estimation for the obtained non-smooth state space model. The operating mechanism of this filtering method makes the mode automatically switchable according to the transformation of operation zone of the system. Simulation and experimental results demonstrate that the proposed non-smooth MKF method achieves higher estimation accuracy for such sandwich systems with backlash affected by random noises than the conventional KF method.
Keywords：backlashsandwich systemsmodified Kalman filternonlinear systemsstate estimation
Publication Date：2016-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 280-288 )