Abstract
This paper presents an application of an Unscented- and a Fuzzy Unscented- Kalman Filter (UKF and FUKF) to the estimation of mechanical state variables and parameters in a drive system with an elastic connection. The cascade control structure incorporating an IP controller supported by two additional feedbacks and suitable adaptation mechanism is investigated in this study. The coefficients of the control structure are retuned on the basis of the value of mechanical parameters estimated by filter. The effectiveness of the proposed approaches (classical and fuzzy) is researched through simulation and experimental tests.
Highlights
Due to the growing demand for high performance drives, the problem of precision control has become a prevalent research subject
A more advanced concept relies on Resonance Ratio Control (RRC) [10,12]
According to the results presented the FUKF works more accurately than the classical UKF
Summary
Due to the growing demand for high performance drives, the problem of precision control has become a prevalent research subject. The following approaches can be distinguished: those relying on neural networks, fuzzy logic and gray estimators [33,34,35] These methodologies do not need information about the mathematical model of the plant, and only training data registered for different operation conditions are necessary. In order to improve the performance of the UKF in the presence of time varying noises, model mismatch and drift forces an adaptation mechanism is proposed It adopts the parameters of the noise covariance based on residual covariance matching methods. Works showing experimental validation of different UKF algorithms in the adaptive control structure of two-mass system seem to be expected by specialists This is the main motivation for the authors. The applied fuzzy system allows us to improve the estimation accuracy of the UKF for different plant parameters as well as in dynamic states. The paper is concluded with some remarks summarizing the findings
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