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Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems

Abstract : We propose a new diffeomorphic matching algorithm and use it to learn nonlinear dynamical systems with the guarantee that the learned systems have global asymptotic stability. For a given set of demonstration trajectories, we compute a diffeomorphism that maps forward orbits of a reference stable time-invariant system onto the demonstrations, thereby deforming the whole reference system into one that reproduces the demonstrations, and is still stable.
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Submitted on : Thursday, November 10, 2016 - 10:08:36 AM
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Nicolas Perrin, Philipp Schlehuber-Caissier. Fast diffeomorphic matching to learn globally asymptotically stable nonlinear dynamical systems. Systems and Control Letters, Elsevier, 2016, 96, pp.51 - 59. ⟨10.1016/j.sysconle.2016.06.018⟩. ⟨hal-01394886⟩

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