Optimal control of general nonlinear systems is a central challenge in automation. Enabled by powerful function approximators, data-driven approaches to have recently successfully tackled challenging applications. However, such methods often obscure the structure dynamics and behind black-box over-parameterized representations, thus limiting our ability understand closed-loop behavior. This art...

Abstract The concept of “hybrid twin” (HT) has recently received a growing interest thanks to the availability powerful machine learning techniques. This twin combines physics-based models within model order reduction framework—to obtain real-time feedback rates—and data science. Thus, main idea HT is develop on-the-fly data-driven correct possible deviations between measurements and prediction...

In mechanical systems coupled with joints, accurate prediction of the joint characteristics is extremely important. Despite years research, a lot yet to be learnt about joints' interface dynamics. The problem becomes even more difficult when Degrees-of-Freedom (DoF) are inaccessible for Frequency Response Function (FRF) measurements. This is, example, case bladed-disk dove-tail or fir-tree type...

Both sales of hybrid vehicles and the number of hybrid models have risen steadily in the U.s. since their introduction, with that growth trend accelerating sharply starting in 2003. The forty-five hybrid models available in 2014 captured about 2.75% of the overall U.s. passenger vehicle market, down slightly from 3.19% in 2013. For purposes of comparison, hybrid market share is about 6% of vehi...

in this paper, we propose a new residual analysis method using fourier series transform into fuzzy time series model for improving the forecasting performance. this hybrid model takes advantage of the high predictable power of fuzzy time series model and fourier series transform to fit the estimated residuals into frequency spectra, select the low-frequency terms, filter out high-frequency term...