Analyzing the coupling process of distributed mixed real-virtual prototypes
CoRR(2024)
Abstract
The ongoing connection and automation of vehicles leads to a closer
interaction of the individual vehicle components, which demands for
consideration throughout the entire development process. In the design phase,
this is achieved through co-simulation of component models. However, complex
co-simulation environments are rarely (re-)used in the verification and
validation phases, in which mixed real-virtual prototypes (e.g.
Hardware-in-the-Loop) are already available. One reason for this are coupling
errors such as time-delays, which inevitably occur in co-simulation of virtual
and real-time systems, and which influence system behavior in an unknown and
generally detrimental way. This contribution introduces a novel, adaptive
method to compensate for constant time-delays in potentially highly nonlinear,
spatially distributed mixed real-virtual prototypes, using small feedforward
neural networks. Their optimal initialization with respect to defined frequency
domain features results from a-priori frequency domain analysis of the entire
coupled system, including coupling faults and compensation methods. A linear
and a nonlinear example demonstrate the method and emphasize its suitability
for nonlinear systems due to online training and adaptation. As the
compensation method requires knowledge only of the bandwidths, the proposed
method is applicable to distributed mixed real-virtual prototypes in general.
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