Learning nonlinear state–space models using autoencoders

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.