@article{xie2022robust,

title = {Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models},

author = {Jing Xie and Fabio Bonassi and Marcello Farina and Riccardo Scattolini},

url = {https://doi.org/10.1002/rnc.6883

http://arxiv.org/abs/2210.06801},

doi = {10.1002/rnc.6883},

year  = {2023},

date = {2023-07-13},

urldate = {2023-07-13},

journal = {International Journal of Robust and Nonlinear Control},

publisher = {arXiv},

abstract = {This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (NNARX) models. The NNARX model learns the dynamics of the plant from input-output data, and during the training the Incremental Input-to-State Stability (𝛿ISS) property is forced to guarantee stability. The trained NNARX model is then augmented with an explicit integral action on the output tracking error, which allows the control scheme to enjoy offset-free tracking ability. A tube-based MPC is finally designed, leveraging the unique structure of the model, to ensure robust stability and robust asymptotic zero error regulation for constant reference signals in the presence of model-plant mismatch or unknown disturbances. Numerical simulations on a water heating system show the effectiveness of the proposed control algorithm.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{bonassi2022survey,

title = {On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments},

author = {Fabio Bonassi and Marcello Farina and Jing Xie and Riccardo Scattolini},

url = {https://arxiv.org/abs/2111.13557},

doi = {10.1016/j.jprocont.2022.04.011},

issn = {0959-1524},

year  = {2022},

date = {2022-01-01},

journal = {Journal of Process Control},

volume = {114},

pages = {92-104},

abstract = {This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications. The main families of RNN are considered, namely Neural Nonlinear AutoRegressive eXogenous, Echo State Networks, Long Short Term Memory, and Gated Recurrent Units. The goal is twofold. Firstly, to survey recent results concerning the training of RNN that enjoy Input-to-State Stability (ISS) and Incremental Input-to-State Stability (𝛿ISS) guarantees. Secondly, to discuss the issues that still hinder the widespread use of RNN for control, namely their robustness, verifiability, and interpretability. The former properties are related to the so-called generalization capabilities of the networks, i.e. their consistency with the underlying real plants, even in presence of unseen or perturbed input trajectories. The latter is instead related to the possibility of providing a clear formal connection between the RNN model and the plant. In this context, we illustrate how ISS and 𝛿ISS represent a significant step towards the robustness and verifiability of the RNN models, while the requirement of interpretability paves the way to the use of physics-based networks. The design of model predictive controllers with RNN as plant’s model is also briefly discussed. Lastly, some of the main topics of the paper are illustrated on a simulated chemical system.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}