Estimation of the mechanical state variables of the two-mass system using fuzzy adaptive Kalman filter - Experimental study

Abstract

This paper investigates the application of fuzzy adaptive Kalman Filter for mechanical state variable and parameter estimation of the drive system with elastic joint.The adaptive state-space controller, which coefficients are retuned according to the estimation parameter provided by Kalman filter, is selected to control the two-mass system effectively. Selected elements of covariance matrix Q are retuned by proposed adaptation law. Additional fuzzy element is used to modified the control law in order to decrease estimation error of the plant. The effectiveness of the proposed approach is investigated under variety of experimental tests.

Improving Low-Frequency Digital Control in the Voltage Source Inverter for the UPS System

“…The Kalman recursive filter [45,[49][50][51][52][53][54] in its basic linear version can be used for the prediction of the state variables. In the state equation v k represents discretized process noise (29).…”

“…The initial conditions can be x 0/0 = [0 0 0] and P 0/0 = p*eye(3) (p is adjusted). The selection of coefficient values of covariance matrices Q and R of the Kalman filter is problematic [52]. A Simulink Kalman filter block was used with Q and R matrices equal to 0.05*eye(3) (Figure 9).…”

A review of nonlinear Kalman filter appling to sensorless control for AC motor drives

“…If the proper fuzzy control rules are designed, the estimation performance of EKF can be improved effectively. Other similar applications of fuzzy theory in tuning system covariance matrix Q are shown in [49]- [51]. The basic idea of fuzzy control is the utilization of the field operator's control experience and knowledge of relevant experts, so the design process of fuzzy control rules is quite complicated.…”

Asenkron Motorun İndi̇rgenmi̇ş-Dereceli̇ Uyarlamali Geni̇şleti̇lmi̇ş Kalman Fi̇ltresi̇ İle Durum Ve Parametre Kesti̇ri̇mi̇

“…Hem hem de 'nin eşzamanlı olarak değiştirilmesi ıraksama veya takip sorunlarına neden olabileceğinden, literatürde bu matrislerden sadece birinin güncellenmesi önerilmiştir [9]. Bu matrisleri bulanık mantık [10], [11] veya uyarlamalı (adaptive) yapıları [12]- [14] kullanarak çalışma koşullarına göre güncelleyen uyarlamalı GKF (UGKF) yapıları önerilmiştir. Bulanık mantık tabanlı UGKF algoritmalarının tasarımı uzmanlık gerektirmesinden dolayı sorunludur.…”