no code implementations • 8 Feb 2024 • Jing Xie, Léo Simpson, Jonas Asprion, Riccardo Scattolini
Temperature control is a complex task due to its often unknown dynamics and disturbances.
no code implementations • 8 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.
no code implementations • 31 Jan 2024 • Marcello Farina, Giancarlo Ferrari-Trecate, Riccardo Scattolini
This report presents three Moving Horizon Estimation (MHE) methods for discrete-time partitioned linear systems, i. e. systems decomposed into coupled subsystems with non-overlapping states.
no code implementations • 21 Oct 2023 • Laura Boca de Giuli, Alessio La Bella, Riccardo Scattolini
This paper addresses the data-based modelling and optimal control of District Heating Systems (DHSs).
no code implementations • 28 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.
no code implementations • 6 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.
no code implementations • 13 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.
no code implementations • 8 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.
no code implementations • 30 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.
no code implementations • 26 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.
no code implementations • 30 Aug 2021 • Xinglong Zhang, Wei Pan, Riccardo Scattolini, Shuyou Yu, Xin Xu
The finite data-driven approximation of Koopman operators results in a class of linear predictors, useful for formulating linear model predictive control (MPC) of nonlinear dynamical systems with reduced computational complexity.
no code implementations • 10 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.
no code implementations • 3 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.
no code implementations • 16 Feb 2021 • Enrico Terzi, Lorenzo Fagiano, Marcello Farina, Riccardo Scattolini
This note extends a recently proposed algorithm for model identification and robust MPC of asymptotically stable, linear time-invariant systems subject to process and measurement disturbances.
no code implementations • 7 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.
no code implementations • 13 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.
no code implementations • 4 Nov 2019 • Simone Pozzoli, Marco Gallieri, Riccardo Scattolini
The use of recurrent neural networks to represent the dynamics of unstable systems is difficult due to the need to properly initialize their internal states, which in most of the cases do not have any physical meaning, consequent to the non-smoothness of the optimization problem.
1 code implementation • 11 Oct 2019 • Pulkit Nahata, Alessio La Bella, Riccardo Scattolini, Giancarlo Ferrari-Trecate
Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distribution Generation Units (DGUs), loads, and power lines.