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Nonlinear Discrete-Time Observers with Physics-Informed Neural Networks
We use Physics-Informed Neural Networks (PINNs) to solve the discrete-time nonlinear observer state estimation problem.
Discrete-Time Nonlinear Feedback Linearization via Physics-Informed Machine Learning
We assess the performance of the proposed PIML approach via a benchmark nonlinear discrete map for which the feedback linearization transformation law can be derived analytically; the example is characterized by steep gradients, due to the presence of singularities, in the domain of interest.
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