Adaptive predictive feedback techniques for vibration by Eure K.W.

By Eure K.W.

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2) is simply white noise. In contrast, the disturbance acting on the actual plant does not 25 manifest itself in the form of white noise added to the plant output. Instead, the white noise is filtered by the dynamics of the plate and the effects of the white noise are observed at the sensor. A closer match between Fig. 4 and Fig. 7 would be obtained if the disturbance was modeled as C(z-1)d(k) rather than just d(k) in Eq. 2). Here, d(k) is white noise and C(z-1) is a polynomial describing the effects of the white noise on the plant as seen by the accelerometer.

The spectra of the open- and closed-loop systems are shown in Fig. 7. 01. 5 KHz. 7 Generalized Predictive Control In Fig. 7, the gray line is the spectrum of the accelerometer output without control and the dotted black line is the output with control. The result is similar to that shown in Fig. 4. In Fig. 7, the vertical axis is not calibrated and is used only to show the closedloop result relative to the open-loop result. At the first mode, around 300 Hz, we see an approximate reduction of around 22 dB while at the second mode we see a reduction of around 13 dB.

As seen in Fig. 3, this greatly attenuated the accelerometer signal due to the sine wave disturbance. 40 dB reduction). The system order was 38 and the sample rate was 10 KHz. 4 shows broadband results using the GPC controller. Here band-limited white noise was applied as the disturbance so no AR disturbance model exists to enhance performance. The black line is the autospectrum of the accelerometer signal when the feedback/feedforward controller of Eq. 9) was used without the second to last term.

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