By Eure K.W.
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Extra info for Adaptive predictive feedback techniques for vibration control
The filters did, however, greatly suppress the higher modes. When sampling any continuous time system, one must chose a sampling rate carefully6. If a continuous-time system is sampled too fast, a poor discrete-time system identification results from the loss of frequency resolution for a fixed order system model. In addition to this, sampling too fast results in the placement of more zeros outside the unit circle which degrades the control performance. If sampling is performed too slow, then the higher frequencies alias40.
Since the disturbance applied to the plant was band-limited white noise, no AR model could be used to predict future disturbances before they enter the plant. Therefore the second to last term in Eq. 9) was dropped. It is also of interest to consider the controller performance around 800 Hz. Here we see that the feedback controller is performing better than the feedback/feedforward controller. This is probably due to the fact that the feedforward path is introducing additional noise into the system around this frequency which is not created by the disturbance.
In this experiment, 256 grams of sand is evenly distributed across the top of the aluminum plate. A system ID is performed for the sand weighted plate and GPC is used to regulate the plate vibrations. 10 shows the autospectrum of the accelerometer signal for the open-loop plate response without sand, gray dotted line, closed-loop without sand, gray line, openloop with sand, dotted line, and closed-loop with sand, black line. In this experiment, seven pole filters with 1 kHz 3db cutoffs were used to low-pass filter both the accelerometer and piezo signals.