Inferring Switched Nonlinear DynamicalSystems
2021
Identification of dynamical and hybrid systems using trajectory data is an important way to construct
models for complex systems where derivation from first principles is too difficult. In this paper, we study the
identification problem for switched dynamical systems with polynomial ODEs. This is a difficult problem as
it combines estimating coefficients for nonlinear dynamics and determining boundaries between modes. We
propose two different algorithms for this problem, depending on whether to perform prior segmentation of
trajectories. For methods with prior segmentation, we present a heuristic segmentation algorithm and a way to
classify themodes using clustering. Formethods without prior segmentation, we extend identification techniques
for piecewise affine models to our problem. To estimate derivatives along the given trajectories, we use Linear
MultistepMethods. Finally, we propose a way to evaluate an identified model by computing a relative difference
between the predicted and actual derivatives. Based on this evaluation method, we perform experiments on
five switched dynamical systems with different parameters, for a total of twenty cases. We also compare with
three baseline methods: clustering with DBSCAN, standard optimization methods in SciPy and identification of
ARX models in Matlab, as well as with state-of-the-art identification method for piecewise affine models. The
experiments show that our two methods perform better across a wide range of situations.
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