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Real-Time Hybrid Substructure (RTHS) testing is a commonly used method to investigate the dynamical influence of a component on a mechanical system. In RTHS, a part of the dynamical system is tested experimentally, while the remaining structure is simulated numerically in a co-simulation. There are several error sources in the RTHS loop that distort the test outcome. To investigate the reliability of the test, the fidelity of the test must be quantified. In many engineering applications, however, there is no reference solution available to which the test outcome can be validated against. This work reviews currently existing accuracy measures used in RTHS. Furthermore, using Artificial Neural Networks (ANN) to predict the fidelity of the RTHS test outcome when no reference solution is available is proposed. Appropriate input features for the network, such as dynamic properties of the system and existing error indicators, are discussed. ANN training was performed on a data set from a virtual RTHS (vRTHS) simulation of a dynamical system with contact. The training process was successful, meaning that the correlation between the ANN prediction and the true fidelity value was > 99 %. Then, the network was applied to data of experimental RTHS tests of the same dynamical system and achieved a correlation of 98 %, which proves that the relation found by the ANN captured the relation between the chosen input features and the error measure. The application of the trained ANN to data from a linear vRTHS test revealed that further improvement of the network and the choice of input features is necessary. This work suggests that ANNs could be a meaningful tool to predict the fidelity of the RTHS test outcome in the absence of a reference solution, especially if more data from different RTHS tests were aggregated to train them.

For a targeted development process of foot prostheses, a profound understanding of the dynamic interaction between humans and prostheses is necessary. In engineering, an often employed method to investigate the dynamics of mechanical systems is Hardware-in-the-Loop (HiL). This study conducted a fundamental investigation of whether HiL could be an applicable method to study the dynamics of an amputee wearing a prosthesis. For this purpose, a suitable HiL setup is presented and the first-ever HiL test of a prosthetic foot performed. In this setup, the prosthetic foot was tested on the test bench and coupled in real-time to a cosimulation of the amputee. The amputee was modeled based on the Virtual Pivot Point (VPP) model, and one stride was performed. The Center of Mass (CoM) trajectory, the Ground Reaction Forces (GRFs), and the hip torque were qualitatively analyzed. The results revealed that the basic gait characteristics of the VPP model can be replicated in the HiL test. Still, there were several limitations in the presented HiL setup, such as the limited actuator performance. The results implied that HiL may be a suitable method for testing foot prostheses. Future work will therefore investigate whether changes in the gait pattern can be observed by using different foot prostheses in the HiL test.