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Learning nonlinear state-space models using autoencoders
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DOI：10.1016/j.automatica.2021.109666.png)
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Abstract
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We propose a methodology for the identification of nonlinear state-space models from input/output data using machine-learning techniques based on autoencoders and neural networks. Our framework simultaneously identifies the nonlinear output and state-update maps of the model. After formulating the approach and providing guidelines for tuning the related hyper-parameters (including the model order), we show its capability in fitting nonlinear models on different nonlinear system identification benchmarks. Performance is assessed in terms of open-loop prediction on test data and of controlling the system via nonlinear model predictive control (MPC) based on the identified nonlinear state-space model. (C) 2021 Elsevier Ltd. All rights reserved.
Keywords:
Identification methods
Model fitting
Identification for control
Neural networks
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