The proposal relates to dynamic systems, such as vehicles, whose motion depends on the excitation applied, with this excitation being generated by environmental or technological effects. Hence, how accurately the excitation is simulated becomes a key point in design and testing. The excitation is often a random signal, i.e. it is not a unique time function but a collection of unpredictable realizations that still can be modeled if inherent characteristics governing behavior of such excitations are understood properly.Modern computer systems made it possible to exercise analytical and experimental simulation of random excitations by digital synthesis. A common technique is to apply the Inverse Fast Fourier Transform (IFFT) with randomized phases to the specified power spectral density (PSD) that describes frequency spectrum of the excitation. That is how a Gaussian random excitation can be modeled. However, not all real excitations are Gaussian and the PSD is not a full description of random signals. There is another basic characteristic - probability density function. If it is different from that of the Gaussian model, the random excitation cannot be simulated by the classic IFFT.The core of the proposed project is a modification of the IFFT simulation to make it non-Gaussian by developing methods of IFFT phase manipulation. The nominated visiting researcher has been working on this problem for many years and his developments on kurtosis control were recognized and implemented by shaker equipment manufacturers in the USA, but not in Europe. An essential part of product development in the host organization also belongs to the shaker testing area. Thus, there will be a transfer of knowledge from the foreign expert to the European host. Then, cooperation between the project participants will aim at using new characteristics more advanced than kurtosis to outclass current non-Gaussian random simulation methods. The latter will also result in joint publications.