Search Results for author: Fabio Bonassi

Found 13 papers, 1 papers with code

On the equivalence of direct and indirect data-driven predictive control approaches

1 code implementation9 Mar 2024 Per Mattsson, Fabio Bonassi, Valentina Breschi, Thomas B. Schön

Recently, several direct Data-Driven Predictive Control (DDPC) methods have been proposed, advocating the possibility of designing predictive controllers from historical input-output trajectories without the need to identify a model.

Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models

no code implementations8 Feb 2024 Jing Xie, Fabio Bonassi, Riccardo Scattolini

This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design.

Structured state-space models are deep Wiener models

no code implementations11 Dec 2023 Fabio Bonassi, Carl Andersson, Per Mattsson, Thomas B. Schön

The goal of this paper is to provide a system identification-friendly introduction to the Structured State-space Models (SSMs).

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

no code implementations28 Sep 2023 Fabio Bonassi, Alessio La Bella, Marcello Farina, Riccardo Scattolini

This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems.

Model Predictive Control

Deep Long-Short Term Memory networks: Stability properties and Experimental validation

no code implementations6 Apr 2023 Fabio Bonassi, Alessio La Bella, Giulio Panzani, Marcello Farina, Riccardo Scattolini

The aim of this work is to investigate the use of Incrementally Input-to-State Stable ($\delta$ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dynamical systems.

Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models

no code implementations13 Oct 2022 Jing Xie, Fabio Bonassi, Marcello Farina, Riccardo Scattolini

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.

Model Predictive Control

Towards lifelong learning of Recurrent Neural Networks for control design

no code implementations8 Aug 2022 Fabio Bonassi, Jing Xie, Marcello Farina, Riccardo Scattolini

This paper proposes a method for lifelong learning of Recurrent Neural Networks, such as NNARX, ESN, LSTM, and GRU, to be used as plant models in control system synthesis.

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

no code implementations30 Mar 2022 Fabio Bonassi, Jing Xie, Marcello Farina, Riccardo Scattolini

This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks.

On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments

no code implementations26 Nov 2021 Fabio Bonassi, Marcello Farina, Jing Xie, Riccardo Scattolini

This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications.

Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems

no code implementations10 Aug 2021 Fabio Bonassi, Riccardo Scattolini

Owing to their superior modeling capabilities, gated Recurrent Neural Networks, such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems.

Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks

no code implementations3 Mar 2021 Fabio Bonassi, C. F. Oliveira da Silva, Riccardo Scattolini

The use of Recurrent Neural Networks (RNNs) for system identification has recently gathered increasing attention, thanks to their black-box modeling capabilities. Albeit RNNs have been fruitfully adopted in many applications, only few works are devoted to provide rigorous theoretical foundations that justify their use for control purposes.

Stability of discrete-time feed-forward neural networks in NARX configuration

no code implementations7 Dec 2020 Fabio Bonassi, Marcello Farina, Riccardo Scattolini

The idea of using Feed-Forward Neural Networks (FFNNs) as regression functions for Nonlinear AutoRegressive eXogenous (NARX) models, leading to models herein named Neural NARXs (NNARXs), has been quite popular in the early days of machine learning applied to nonlinear system identification, owing to their simple structure and ease of application to control design.

On the stability properties of Gated Recurrent Units neural networks

no code implementations13 Nov 2020 Fabio Bonassi, Marcello Farina, Riccardo Scattolini

The goal of this paper is to provide sufficient conditions for guaranteeing the Input-to-State Stability (ISS) and the Incremental Input-to-State Stability ({\delta}ISS) of Gated Recurrent Units (GRUs) neural networks.

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