USING THE FOUR-POLE REPRESENTATION OF THE POINCARE-STEKLOV FILTER FOR HARDWARE IN THE LOOP SIMULATION OF NONLINEAR SYSTEMS
Abstract
The article shows the possibility of using the Poincare-Steklov filter to ensure the stability of
harware in the loop (HIL) simulation of nonlinear systems.HIL simulation involves splitting the
initial system into parts, with one part being modeled numerically on a computer, and the second
part is represented by a real physical object. The parts of the system exchange data with each
other through a hardware-software interface, which can be implemented in different ways and
should ensure stability, as well as convergence of the results of HIL simulation to the results of
modeling the original system. The variants of constructing software and hardware interfaces ITM,
TLM, TFA, PCD, DIM, GCS and the Poincare-Steklov filter are described in the relevant literature
sources. The article shows how the original nonlinear system was divided into parts using the
Poincare-Steklov filter, which, accordingly, led to the splitting into parts of the system of equations
describing the behavior of the original system. Next, it was shown how the values of the stabilizing
parameters of the Poincare-Steklov filter were calculated and the systems of equations of the system
divided into parts were corrected in accordance with the obtained values. At the next stage,
the article presents the results of numerical modeling of the initial and partitioned system in
MATLAB. When modeling in parts, the parts of the system exchanged data with each other at each
step of the simulation only once with a delay of h. This method of numerical modeling of a system
divided into parts is as close as possible to the processes occurring during semi-natural modeling
of systems. A comparison of the obtained simulation results of the initial and the system divided
into parts allowed us to conclude that the Poincare-Steklov filter, with the correct choice of the
values of the stabilizing parameters, allows for the stability and convergence of the results of seminatural
modeling of both linear and nonlinear systems
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