Prediction of system behavior by sinusoidal extrapolation prediction filter

Predicting the response of a system, even several steps ahead, offers tremendous advantage to improve the system performance, to acquire an ideal model of a system and disturbances. The best way of predicting a response signal from a system is to use the sinusoidal extrapolation based on its frequency characteristics. Sinusoidal extrapolation is a statistical method for predicting future data through frequency analysis of past data. Practically speaking, the prediction from a frequency analysis in a control system is appropriate, because the output of a system can be modeled by several dominant frequencies from input and system models. In this study, we developed a novel and reliable prediction filter, using multi frequency sinusoidal extrapolation and a prediction error compensation algorithm. In this paper, we also suggest the design guidelines, regularity, and overall process of obtaining optimal predictions from an efficient and practical view, for the widely used industrial equipment. Results show that the performance of the proposed prediction filter is considered reliable and effective for improving the performance of a system, such as a motion controller.