An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models

@article{Bonassi2022AnON,
  title={An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models},
  author={Fabio Bonassi and Jing Xie and Marcello Farina and Riccardo Scattolini},
  journal={ArXiv},
  year={2022},
  volume={abs/2203.16290}
}
—This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXoge- nous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability ( δ ISS… 

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