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By Eure K.W.

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This is not surprising since the plant model from which the controller was designed was used in the simulations so the plant model is exact and the actual plant was used in the experiment. Also, in the experiment noise is present whereas the simulations are noise free. For both the experiment and simulation, performance may be increased by increasing the prediction horizon and decreasing the control penalty as long as stability is maintained. 5 shows the time history of the experimental output data.

9). 2 shows the autospectrum of plant output (accelerometer signal, y) without control (gray line), feedback only (dotted line), and with feedback/feedforward control (solid line). 5 KHz. Both control and prediction horizons were set to 12. 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, 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. Deadbeat Predictive Control was also tested in the same setup. The spectrum results of the open- and closed-loop accelerometer responses are shown in Fig. 8. 8 Deadbeat Predictive Control The gray line is the open-loop accelerometer output and the black dotted line is the closed-loop. From Fig. 8 both modes are lowered to around the same level. The mode at 300 Hz has an approximate reduction of 15 dB while the 1 KHz mode is reduced by around 13 dB.

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