Fabio Bonassi

Learning Control Affine Neural NARX Models for Internal Model Control Design

Xie, Jing; Bonassi, Fabio; Scattolini, Riccardo

Ingår i IEEE Transactions on Automation Science and Engineering, s. 8137-8149, 2025

• DOI för Learning Control Affine Neural NARX Models for Internal Model Control Design
• Ladda ner fulltext (pdf) av Learning Control Affine Neural NARX Models for Internal Model Control Design

Learning state observers for recurrent neural network models

Bonassi, Fabio; Andersson, Carl; Mattsson, Per; Schön, Thomas B.

Ingår i 2024 IEEE 63rd Conference on Decision and Control (CDC), s. 7871-7877, 2024

• DOI för Learning state observers for recurrent neural network models

Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances

Masero, Eva; Bonassi, Fabio; La Bella, Alessio; Scattolini, Riccardo

Ingår i 2024 IEEE 63rd Conference on Decision and Control (CDC), s. 120-125, 2024

• DOI för Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances

Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

Bonassi, Fabio; Andersson, Carl; Mattsson, Per; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 247-252, 2024

• DOI för Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Mattsson, Per; Bonassi, Fabio; Breschi, Valentina; Schön, Thomas B.

Ingår i IEEE Control Systems Letters, s. 796-801, 2024

• DOI för On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Learning Control Affine Neural NARX Models for Internal Model Control Design

Xie, Jing; Bonassi, Fabio; Scattolini, Riccardo

Ingår i IEEE Transactions on Automation Science and Engineering, s. 8137-8149, 2025

• DOI för Learning Control Affine Neural NARX Models for Internal Model Control Design
• Ladda ner fulltext (pdf) av Learning Control Affine Neural NARX Models for Internal Model Control Design

Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

Bonassi, Fabio; Andersson, Carl; Mattsson, Per; Schön, Thomas B.

Ingår i IFAC-PapersOnLine, s. 247-252, 2024

• DOI för Structured state-space models are deep Wiener modelsStructured state-space models are deep Wiener models

On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Mattsson, Per; Bonassi, Fabio; Breschi, Valentina; Schön, Thomas B.

Ingår i IEEE Control Systems Letters, s. 796-801, 2024

• DOI för On the Equivalence of Direct and Indirect Data-Driven Predictive Control Approaches

Nonlinear MPC design for incrementally ISS systems with application to GRU networks

Bonassi, Fabio; La Bella, Alessio; Farina, Marcello; Scattolini, Riccardo

Ingår i Automatica, 2024

• DOI för Nonlinear MPC design for incrementally ISS systems with application to GRU networks

Reconciling Deep Learning and Control Theory: Recurrent Neural Networks for Indirect Data-Driven Control

Bonassi, Fabio

Ingår i Special Topics in Information Technology, Springer, 2024

• DOI för Reconciling Deep Learning and Control Theory: Recurrent Neural Networks for Indirect Data-Driven Control

Learning state observers for recurrent neural network models

Bonassi, Fabio; Andersson, Carl; Mattsson, Per; Schön, Thomas B.

Ingår i 2024 IEEE 63rd Conference on Decision and Control (CDC), s. 7871-7877, 2024

• DOI för Learning state observers for recurrent neural network models

Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances

Masero, Eva; Bonassi, Fabio; La Bella, Alessio; Scattolini, Riccardo

Ingår i 2024 IEEE 63rd Conference on Decision and Control (CDC), s. 120-125, 2024

• DOI för Estimation and MPC control based on gated recurrent unit neural networks with unknown disturbances