95 citations · 326 across the 12 of their papers we have counts for
12 papers
Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models
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. The id…
Nonlinear MPC design for incrementally ISS systems with application to GRU networks
Fabio Bonassi, Alessio La Bella, Marcello Farina +1
This brief addresses the design of a Nonlinear Model Predictive Control (NMPC) strategy for exponentially incremental Input-to-State Stable (ISS) systems. In particular, a novel fo…
Deep Long-Short Term Memory networks: Stability properties and Experimental validation
Fabio Bonassi, Alessio La Bella, Giulio Panzani +2
The aim of this work is to investigate the use of Incrementally Input-to-State Stable (ISS) deep Long Short Term Memory networks (LSTMs) for the identification of nonlinear dyna…
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models
Jing Xie, Fabio Bonassi, Marcello Farina +1
This paper presents a robust Model Predictive Control (MPC) scheme that provides offset-free setpoint tracking for systems described by Neural Nonlinear AutoRegressive eXogenous (N…
Towards lifelong learning of Recurrent Neural Networks for control design
Fabio Bonassi, Jing Xie, Marcello Farina +1
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. The prob…
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models
Fabio Bonassi, Jing Xie, Marcello Farina +1
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) n…
On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments
Fabio Bonassi, Marcello Farina, Jing Xie +1
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…
Recurrent Neural Network-based Internal Model Control design for stable nonlinear systems
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 popula…
Nonlinear MPC for Offset-Free Tracking of systems learned by GRU Neural Networks
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 b…
Stability of discrete-time feed-forward neural networks in NARX configuration
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 (NN…
On the stability properties of Gated Recurrent Units neural networks
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 (δISS) of Gated Recurren…
LSTM Neural Networks: Input to State Stability and Probabilistic Safety Verification
Fabio Bonassi, Enrico Terzi, Marcello Farina +1
The goal of this paper is to analyze Long Short Term Memory (LSTM) neural networks from a dynamical system perspective. The classical recursive equations describing the evolution o…