Keywords: predictive control, noise reduction, time delay, time compensate.

The theories and applications on structural control were proved with excellent effect on energy dissipation by experiments. The active and semi-active controls [1,2,3] depend on the response of system vibration, which requires a core unit to control and create signals. They precisely organize the sequence of signals as a query of the dynamic response of the structure and properly change over active or semi-active components according to the response data. Thus, the quality of the signals is definitely responsible for the success of the control system. Recently, the optimal prediction theory [4,5,6,7,8] and Kalman filter [9,10,11] have provided a signal-processing methodology to anticipate the response of structures. However, noises may occur while accessing and transporting the signals, it will be the key to failure or success of a control system.

Therefore, the predictive control and signal noise reduction technology is proposed in this research based on the direction of structural motions. A velocity prediction is developed by the least-square polynomial regression. This methodology is appropriate for the semi-active damper system. It decides the optimal switch point of control elements based on the current and past reaction data of the structural response under the excitation of external forces. Comparing with experimental data, the velocity detector (or predictor) is available to detect when to activate or switch the semi-active damper. It can detect when the structure motion reverses the direction as well as eliminates the poor influence on the semi-active damper caused by the compensated time delay. Meanwhile, the noise of displacement signal will not affect the phase difference of the predictive signal.

This proposed technology is appropriate for the control system with the following characteristics: (1) noises in measured signals, (2) control forces that are not continuous, (3) when the structure and control system are not linear, and (4) when the exact signal phase is required to precisely query the switching moment.

1

Chassiakoes, GW, et el., "Structural control: past present and future", Journal of Engineering Mechanics 1997; 123(9): 897-971. doi:10.1061/(ASCE)0733-9399(1997)123:9(897)

2

Yao, JTP, "Concept of structural control", Journal of the Structural Division 1972; 123(9): 1567-1574.

3

Meirovitch, L., "Dynamics and control of structures", John Wiley & Sons; 1990.

4

Ernst, D, "Noise prediction equalizer and decision feedback equalizer for high speed digital subscriber line transmission - a comparison of performance", European Transactions on Telecommunications and Related Technology 1993; 4(1): 63-67. doi:10.1002/ett.4460040113

5

Schober, R., Gerstacker, WH, Huber, JB, "Improving differential detection MDPSK by noise prediction", IEEE Global Telecommunications Conference 1998; 1: 567-572. doi:10.1109/GLOCOM.1998.775791

6

Kjell, G, "Predicting response spectra for earthquake signals generated as filtered noise", Probabilistic Engineering Mechanics 2002; 17(3): 241-252. doi:10.1016/S0266-8920(02)00009-7

7

Bolcskei, H., Hlawatsch, F., "Noise reduction in oversampled filter banks using predictive quantization", IEEE Transactions on Information Theory 2001; 47(1): 155-172. doi:10.1109/18.904519

8

Huang HG, Chen CM, Wang SD, Lu HH. "Adaptive symmetric mean filter: A new noise-reduction approach based on the slope facet model", Applied Optics 2001; 40(29): 5192-5205. doi:10.1364/AO.40.005192

9

Kalman, RE, "A new approach to linear filtering and prediction problems", Transactions of the ASME-Journal of Basic Engineering 1960; 82 (Series D): 35-45.

10

Drecourt, J.P., "Literature review on Kalman filtering", http://www.cs.unc.edu/~welch/kalman, May 29, 2003.

11

Paragios, N. Deriche, R., "Geodesic active contours and level sets for the detection and tracking of moving objects", IEEE Transactions on Pattern Analysis and Machine Intelligence 2000; 22(3): 266-280. doi:10.1109/34.841758

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