  Dynamic models of mechatronic systems are abundantly used in the context of
motion control and design of complex servo applications. In practice, these
systems are often plagued by unknown interactions, which make the physics-based
relations of the system dynamics only partially known. This paper presents a
neural network augmented physics (NNAP) model as a combination of
physics-inspired and neural layers. The neural layers are inserted in the model
to compensate for the unmodeled interactions, without requiring direct
measurements of these unknown phenomena. In contrast to traditional approaches,
both the neural network and physical parameters are simultaneously optimized,
solely by using state and control input measurements. The methodology is
applied on experimental data of a slider-crank setup for which the state
dependent load interactions are unknown. The NNAP model proves to be a stable
and accurate modeling formalism for dynamic systems that ab initio can only be
partially described by physical laws. Moreover, the results show that a
recurrent implementation of the NNAP model enables improved robustness and
accuracy of the system state predictions, compared to its feedforward
counterpart. Besides capturing the system dynamics, the NNAP model provides a
means to gain new insights by extracting the neural network from the converged
NNAP model. In this way, we discovered accurate representations of the unknown
spring force interaction and friction phenomena acting on the slider mechanism.
