Investigation of the dynamic behavior of nonlinear mechanical and mechatronic systems by SINDy and Artificial Neural Networks

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There is a permanent interest on the research of the dynamics of mechanical and mechatronic systems. Beside classical methods of modeling and model identification new methods related with keywords like “Big Data”, “Deep learning” or “Artificial Intelligence” have attracted much attention in many scientific disciplines. Nevertheless, the application of such methods on mechanical and mechatronic disciplines is still in its infancy. From literature two methods, namely Sparse Identification of Nonlinear Dynamics (SINDy) and Artificial Neural Networks (ANN) are considered. The aim is, to adapt and to further develop these two methods initially to a small number of fundamental experimental set-ups. For one experimental set-up, an energy harvesting system consisting of a cantilever beam with magnets, there is already some experience in the working group. Describing its dynamic behavior has been turned out to be problematic which makes it a good example for applying the two methods. As far as possible, knowledge about physical properties shall be taken into account. A thorough comparison of experimental and simulation results is planned. In further steps, the results shall be applied to more complicated systems, e.g. a brake.