Non-linear System Identification from Partial Observations via Iterative Smoothing and LearningDownload PDF

25 Sep 2019 (modified: 24 Dec 2019)ICLR 2020 Conference Blind SubmissionReaders: Everyone
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  • TL;DR: This work presents a scalable algorithm for non-linear offline system identification from partial observations.
  • Abstract: System identification is the process of building a mathematical model of an unknown system from measurements of its inputs and outputs. It is a key step for model-based control, estimator design, and output prediction. This work presents an algorithm for non-linear offline system identification from partial observations, i.e. situations in which the system's full-state is not directly observable. The algorithm presented, called SISL, iteratively infers the system's full state through non-linear optimization and then updates the model parameters. We test our algorithm on a simulated system of coupled Lorenz attractors, showing our algorithm's ability to identify high-dimensional systems that prove intractable for particle-based approaches. We also use SISL to identify the dynamics of an aerobatic helicopter. By augmenting the state with unobserved fluid states, we learn a model that predicts the acceleration of the helicopter better than state-of-the-art approaches.
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  • Keywords: System Identification, Dynamical Systems, Partial Observations, Non-linear Programming, Expectation Maximization, Neural Networks
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