187 citations · 532 across the 25 of their papers we have counts for
26 papers · 1 filter
Moving horizon partition-based state estimation of large-scale systems -- Revised version
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-overlappin…
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…
Robust Tube-based Model Predictive Control with Koopman Operators--Extended Version
Xinglong Zhang, Wei Pan, Riccardo Scattolini +2
Koopman operators are of infinite dimension and capture the characteristics of nonlinear dynamics in a lifted global linear manner. The finite data-driven approximation of Koopman…
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…
Robust multi-rate predictive control using multi-step prediction models learned from data
Enrico Terzi, Lorenzo Fagiano, Marcello Farina +1
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 d…
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…
Hierarchical routing control in discrete manufacturing plants via model predictive path allocation and greedy path following
Lorenzo Fagiano, Marko Tanaskovic, Lenin Cucas Mallitasig +2
The problem of real-time control and optimization of components' routing in discrete manufacturing plants, where distinct items must undergo a sequence of jobs, is considered. This…
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…
Supervised MPC control of large-scale electricity networks via clustering methods
Alessio La Bella, Pascal Klaus, Giancarlo Ferrari-Trecate +1
This paper describes a control approach for large-scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations…
Scenario optimization for optimal training of Echo State Networks
Luca Bugliari Armenio, Lorenzo Fagiano, Enrico Terzi +2
Echo State Networks (ESNs) are widely-used Recurrent Neural Networks. They are dynamical systems including, in state-space form, a nonlinear state equation and a linear output tran…
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…
Hierarchical Control in Islanded DC Microgrids with Flexible Structures
Pulkit Nahata, Alessio La Bella, Riccardo Scattolini +1
Hierarchical architectures stacking primary, secondary, and tertiary layers are widely employed for the operation and control of islanded DC microgrids (DCmGs), composed of Distrib…
Model predictive control design for dynamical systems learned by Long Short-Term Memory Networks
Enrico Terzi, Fabio Bonassi, Marcello Farina +1
This paper analyzes the stability-related properties of Long Short-Term Memory (LSTM) networks and investigates their use as the model of the plant in the design of Model Predictiv…
Echo State Networks: analysis, training and predictive control
Luca Bugliari Armenio, Enrico Terzi, Marcello Farina +1
The goal of this paper is to investigate the theoretical properties, the training algorithm, and the predictive control applications of Echo State Networks (ESNs), a particular kin…
Learning-based predictive control for linear systems: a unitary approach
Enrico Terzi, Lorenzo Fagiano, Marcello Farina +1
A comprehensive approach addressing identification and control for learningbased Model Predictive Control (MPC) for linear systems is presented. The design technique yields a data-…
On multi-step prediction models for receding horizon control
Enrico Terzi, Lorenzo Fagiano, Marcello Farina +1
The derivation of multi-step-ahead prediction models from sampled data of a linear system is considered. A dedicated prediction model is built for each future time step of interest…
A hierarchical multirate MPC scheme for interconnected systems -
Marcello Farina, Xinglong Zhang, Riccardo Scattolini
This paper presents a hierarchical control scheme for interconnected linear systems. At the higher layer of the control structure a robust centralized Model Predictive Control (MPC…
A hierarchical MPC scheme for interconnected systems
Marcello Farina, Xinglong Zhang, Riccardo Scattolini
This paper describes a hierarchical control scheme for interconnected systems. The higher layer of the control structure is designed with robust Model Predictive Control (MPC) base…
An MPC approach to output-feedback control of stochastic linear discrete-time systems
Marcello Farina, Luca Giulioni, Lalo Magni +1
In this paper we propose an output-feedback Model Predictive Control (MPC) algorithm for linear discrete-time systems affected by a possibly unbounded additive noise and subject to…