D. Piga

发表

Nonparametric LPV data-driven control

Sergio M. Savaresi, Simone Formentin, Dario Piga, 2015 .

Direct data-driven control of linear parameter-varying systems

Sergio M. Savaresi, Simone Formentin, Roland Tóth, 2013, 52nd IEEE Conference on Decision and Control.

High-altitude wind energy generation using controlled tethered airfoils

Lorenzo Fagiano, Mario Milanese, Dario Piga, 2010 .

Direct Data-Driven Control of Constrained Systems

A. Bemporad, S. Formentin, D. Piga, 2016, IEEE Transactions on Control Systems Technology.

A unified framework for deterministic and probabilistic D-stability analysis of uncertain polynomial matrices

D. Piga, A. Benavoli, 2017 .

7 Model Predictive Control in Discrete Manufacturing Shopfloors

D. Piga, G. Montalbano, A. Brusaferri, 2019 .

PiecewiseAffineRegression viaRecursiveMultiple Least Squares andMulticategoryDiscrimination ⋆

A. Bemporad, D. Piga, V. Breschi, 2016 .

An InstrumentalLeast Squares SupportVectorMachine for Nonlinear SystemIdentification ⋆

R. Tóth, W. Zheng, D. Piga, 2015 .

Fitting JumpModels

Stephen P. Boyd, A. Bemporad, D. Piga, 2018 .

Identification of elasto-plastic and nonlinear fracture mechanics parameters of silver-plated copper busbars for photovoltaics

D. Piga, M. Paggi, C. Borri, 2019, Engineering Fracture Mechanics.

Bounding the parameters of block‐structured nonlinear feedback systems

Dario Piga, Vito Cerone, Diego Regruto, 2013 .

Direct learning of LPVcontrollers fromdata ?

R. Tóth, S. Formentin, D. Piga, 2015 .

Estimation of JumpBox-JenkinsModels

A. Bemporad, D. Piga, V. Breschi, 2020 .

Bounded-Error Identification of Linear Systems with Input and Output Backlash

V. Cerone, D. Regruto, D. Piga, 2012 .

Approximation of Model Predictive Control Laws for Polynomial Systems

V. Cerone, D. Regruto, D. Piga, 2014 .

SOS FOR BOUNDED RATIONALITY SOS for bounded rationality

Marco Zaffalon, D. Piga, A. Benavoli, 2017 .

LPVsystem identificationunder noise corrupted scheduling andoutput signal observations ⋆

R. Tóth, D. Piga, V. Laurain, 2014 .

Astochastic interpretationof set-membershipfiltering : application to polynomial systems throughpolytopic bounding ⋆

D. Piga, A. Benavoli, 2018 .

A Bias-Correction Approach for the Identification of Piecewise Affine Output-Error Models

Vihangkumar V. Naik, D. Piga, Manas Mejari, 2020 .

Segmentation of ARX models through SDP-relaxation techniques

D. Piga, 2010 .

Active preference-based optimization for human-in-the-loop feature selection

A. Bemporad, L. Piroddi, D. Piga, 2022, Eur. J. Control.

Learning neural state-space models: do we need a state estimator?

D. Piga, Manas Mejari, Marco Forgione, 2022, ArXiv.

A survey on computational taste predictors

K. Theofilatos, D. Piga, Gianvito Grasso, 2022, European Food Research and Technology.

A survey on computational taste predictors

K. Theofilatos, D. Piga, Gianvito Grasso, 2022, European Food Research and Technology.

Computational burden reduction in Set-membership Hammerstein system identification

V. Cerone, D. Regruto, D. Piga, 2011 .

Astochasticinterpretationofset-membershipfiltering: applicationtopolynomialsystemsthroughpolytopic bounding ⋆

D. Piga, A. Benavoli, Dalle Molle, 2015 .

EnforcingstabilityconstraintsinSet-membership identificationoflineardynamicsystems

V. Cerone, D. Regruto, D. Piga, 2011 .

Bounding the parameters of linear systems with stability constraints

V. Cerone, D. Regruto, D. Piga, 2010, Proceedings of the 2010 American Control Conference.

An instrumental Least Squares Support Vector Machine for system identification

Dario Piga, Vincent Laurain, Roland Toth, 2013, ICML 2013.

Direct identification of continuous-time linear switched state-space models

D. Piga, Manas Mejari, 2022, ArXiv.

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

M. Piepoli, D. Piga, M. Deriu, 2021, Journal of medical Internet research.

Visual Servoing with Geometrically Interpretable Neural Perception

A. Giusti, D. Piga, Antonio Paolillo, 2022, 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Computation of Parameter Dependent Robust Invariant Sets for LPV Models with Guaranteed Performance

P. Falcone, D. Piga, Ankit Gupta, 2020, Autom..

Characteristic polynomial assignment for plants with semialgebraic uncertainty: A robust diophantine equation approach

V. Cerone, D. Regruto, D. Piga, 2015 .

Model Structures and Fitting Criteria for System Identification with Neural Networks

D. Piga, Marco Forgione, 2019, 2020 IEEE 14th International Conference on Application of Information and Communication Technologies (AICT).

Learning hybrid models with logical and continuous dynamics via multiclass linear separation

Alberto Bemporad, Dario Piga, Valentina Breschi, 2016, 2016 IEEE 55th Conference on Decision and Control (CDC).

A probabilistic interpretation of set-membership filtering: Application to polynomial systems through polytopic bounding

Alessio Benavoli, Dario Piga, D. Piga, 2015, Autom..

Aprobabilistic interpretationof set-membershipfiltering : application to polynomial systems throughpolytopic bounding ⋆

Alessio Benavoli, Dario Piga, D. Piga, 2016 .

A stochastic interpretation of set-membership filtering: application to polynomial systems through polytopic bounding pdf

Alessio Benavoli, Dario Piga, D. Piga, 2015, ArXiv.

Polytopic outer approximations of semialgebraic sets

Dario Piga, Vito Cerone, Diego Regruto, 2012, 2012 IEEE 51st IEEE Conference on Decision and Control (CDC).

Fixed-order FIR approximation of linear systems from quantized input and output data

Dario Piga, Vito Cerone, Diego Regruto, 2013, Syst. Control. Lett..

Sparse RKHS estimation via globally convex optimization and its application in LPV-IO identification

Dario Piga, Vincent Laurain, Roland Tóth, 2020, Autom..

Minimal LPV state-space realization driven set-membership identification

Roland Tóth, Dario Piga, Vito Cerone, 2012, 2012 American Control Conference (ACC).

Human-robot collaboration in sensorless assembly task learning enhanced by uncertainties adaptation via Bayesian Optimization

Dario Piga, Loris Roveda, Mauro Magni, 2021, Robotics Auton. Syst..

Assembly Task Learning and Optimization through Human’s Demonstration and Machine Learning

Giuseppe Bucca, Dario Piga, Loris Roveda, 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

On the human taste perception: Molecular-level understanding empowered by computational methods

Giacomo Di Benedetto, Dario Piga, Seferina Mavroudi, 2021 .

Estimation of jump Box-Jenkins models

Alberto Bemporad, Dario Piga, Valentina Breschi, 2020, Autom..

Recursive Bias-Correction Method for Identification of Piecewise Affine Output-Error Models

Dario Piga, Valentina Breschi, Manas Mejari, 2020, IEEE Control Systems Letters.

Identification of hybrid and linear parameter‐varying models via piecewise affine regression using mixed integer programming

Alberto Bemporad, Dario Piga, Vihangkumar V. Naik, 2020, International Journal of Robust and Nonlinear Control.

Rao-Blackwellized sampling for batch and recursive Bayesian inference of Piecewise Affine models

A. Bemporad, A. Benavoli, D. Piga, 2020, Autom..

Prediction error methods in learning jump ARMAX models

Stephen P. Boyd, Alberto Bemporad, Dario Piga, 2018, 2018 IEEE Conference on Decision and Control (CDC).

Regularized moving-horizon piecewise affine regression using mixed-integer quadratic programming

Alberto Bemporad, Dario Piga, Manas Mejari, 2017, 2017 25th Mediterranean Conference on Control and Automation (MED).

Computation of the Structured Singular Value via Moment LMI Relaxations

Dario Piga, D. Piga, 2016, IEEE Transactions on Automatic Control.

C-GLISp: Preference-Based Global Optimization Under Unknown Constraints With Applications to Controller Calibration pdf

Alberto Bemporad, Dario Piga, Mengjia Zhu, 2021, IEEE Transactions on Control Systems Technology.

Global optimization based on active preference learning with radial basis functions

Alberto Bemporad, Dario Piga, A. Bemporad, 2020, Mach. Learn..

Active preference learning based on radial basis functions pdf

Alberto Bemporad, Dario Piga, A. Bemporad, 2019, ArXiv.

Efficient Calibration of Embedded MPC

A. Bemporad, D. Piga, Marco Forgione, 2019, ArXiv.

Performance-Oriented Model Learning for Data-Driven MPC Design

Alberto Bemporad, Marco Forgione, Simone Formentin, 2019, IEEE Control Systems Letters.

Direct Data-Driven Control of Constrained Systems

Alberto Bemporad, Simone Formentin, Dario Piga, 2018, IEEE Transactions on Control Systems Technology.

Fast implementation of model predictive control with guaranteed performance

Massimo Canale, Dario Piga, Vito Cerone, 2011, IEEE Conference on Decision and Control and European Control Conference.

Bounded error identification of Hammerstein systems through sparse polynomial optimization

V. Cerone, D. Regruto, D. Piga, 2012, Autom..

A convex relaxation approach to set-membership identification of LPV systems

Dario Piga, Vito Cerone, Diego Regruto, 2012, Autom..

Bounded error identification of Hammerstein systems with backlash

Dario Piga, Vito Cerone, Diego Regruto, 2010 .

Parameter bounds evaluation for linear system with output backlash

Dario Piga, Vito Cerone, Diego Regruto, 2009 .

Piecewise affine regression via recursive multiple least squares and multicategory discrimination

Alberto Bemporad, Dario Piga, Valentina Breschi, 2016, Autom..

Sum-of-squares for bounded rationality

Marco Zaffalon, Alessio Benavoli, Alessandro Facchini, 2019, Int. J. Approx. Reason..

A bias-corrected estimator for nonlinear systems with output-error type model structures

Roland Tóth, Dario Piga, R. Tóth, 2014, Autom..

Sensorless environment stiffness and interaction force estimation for impedance control tuning in robotized interaction tasks

Dario Piga, Loris Roveda, D. Piga, 2021, Autonomous Robots.

Kalman filtering for energy disaggregation

Alberto Bemporad, Dario Piga, Valentina Breschi, 2018 .

A Unified Framework for Solving a General Class of Conditional and Robust Set-Membership Estimation Problems

Jean B. Lasserre, Dario Piga, Vito Cerone, 2014, IEEE Transactions on Automatic Control.

Decentralized Multi-Agent Control of a Manipulator in Continuous Task Learning

Asad Ali Shahid, D. Piga, F. Braghin, 2021, Applied Sciences.

Continuous-time system identification with neural networks: model structures and fitting criteria

D. Piga, Marco Forgione, 2020, Eur. J. Control.

Integrated Neural Networks for Nonlinear Continuous-Time System Identification

Marco Forgione, Dario Piga, Bojan Mavkov, 2020, IEEE Control Systems Letters.

Neural State-Space Models: Empirical Evaluation of Uncertainty Quantification

D. Piga, Marco Forgione, 2023, IFAC-PapersOnLine.

LPV identification of the glucose-insulin dynamics in Type I diabetes

Dario Piga, Vito Cerone, Diego Regruto, 2012 .

Set-membership LPV model identification of vehicle lateral dynamics

Dario Piga, Vito Cerone, Diego Regruto, 2011, Autom..

Enforcing stability constraints in set-membership identification of linear dynamic systems

Dario Piga, Vito Cerone, Diego Regruto, 2011, Autom..

LPV system identification under noise corrupted scheduling and output signal observations

Roland Tóth, Dario Piga, Vincent Laurain, 2015, Autom..

LPVsystemidentic ationundernoisecorruptedscheduling andoutputsignalobservations

Dario Piga, Pb Pepijn Cox, T Roland, 2014 .

Toward a general and interpretable umami taste predictor using a multi-objective machine learning approach

K. Theofilatos, D. Piga, Gianvito Grasso, 2022, Scientific reports.

High-Altitude Wind Power Generation

L. Fagiano, M. Milanese, D. Piga, 2010, IEEE Transactions on Energy Conversion.

A Hybrid Signature-based Iterative Disaggregation algorithm for Non-Intrusive Load Monitoring

Andrea Castelletti, Andrea Emilio Rizzoli, Matteo Giuliani, 2017 .

Benefits and challenges of using smart meters for advancing residential water demand modeling and management: A review

Andrea Castelletti, Andrea Emilio Rizzoli, Matteo Giuliani, 2015, Environ. Model. Softw..

Learning Continuous Control Actions for Robotic Grasping with Reinforcement Learning

Francesco Braghin, Dario Piga, Loris Roveda, 2020, 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

Two-Stage Robot Controller Auto-Tuning Methodology for Trajectory Tracking Applications

D. Piga, L. Roveda, Marco Forgione, 2020 .

Input-Output LPV Model identification with guaranteed quadratic stability

Dario Piga, Vito Cerone, Diego Regruto, 2012 .

Grasping learning, optimization, and knowledge transfer in the robotics field

A. Pedrocchi, D. Piga, F. Braghin, 2022, Scientific Reports.

Fragmented blind docking: a novel protein–ligand binding prediction protocol

D. Piga, M. Deriu, A. Danani, 2021, Journal of biomolecular structure & dynamics.

Continuous-time linear time-varying system identification with a frequency-domain kernel-based estimator

Rik Pintelon, Dario Piga, Roland Toth, 2017 .

Frequency-domain least-squares support vector machines to deal with correlated errors when identifying linear time-varying systems

Dario Piga, Roland Toth, Jpg Lataire, 2014 .

Choice functions based multi-objective Bayesian optimisation pdf

Alessio Benavoli, Dario Piga, Dario Azzimonti, 2021, ArXiv.

Maximum-a-posteriori estimation of jump Box-Jenkins models

Alberto Bemporad, Dario Piga, Valentina Breschi, 2019, 2019 IEEE 58th Conference on Decision and Control (CDC).

Model structure selection for switched NARX system identification: A randomized approach

Luigi Piroddi, Dario Piga, Federico Bianchi, 2021, Autom..

A Unified Framework for Deterministic and Probabilistic $\mathscr {D}$-Stability Analysis of Uncertain Polynomial Matrices

Alessio Benavoli, Dario Piga, D. Piga, 2016, IEEE Transactions on Automatic Control.

Fitting Jump Models pdf

Stephen P. Boyd, Alberto Bemporad, Dario Piga, 2017, Autom..

A Machine Learning Approach for Mortality Prediction in COVID-19 Pneumonia: Development and Evaluation of the Piacenza Score

M. Piepoli, D. Piga, M. Deriu, 2021, Journal of Medical Internet Research.

A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification

Umberto Michelucci, Dario Piga, Francesca Venturini, 2021, Algorithms.

Learning Choice Functions with Gaussian Processes

D. Piga, A. Benavoli, Dario Azzimonti, 2023, arXiv.org.

Preferential Bayesian optimisation with skew gaussian processes pdf

Alessio Benavoli, Dario Piga, Dario Azzimonti, 2020, GECCO Companion.

A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian Processes pdf

Alessio Benavoli, Dario Piga, Dario Azzimonti, 2020, Machine Learning.

Skew Gaussian processes for classification pdf

Alessio Benavoli, Dario Piga, Dario Azzimonti, 2020, Machine Learning.

Robot End-Effector Mounted Camera Pose Optimization in Object Detection-Based Tasks

Giuseppe Bucca, Dario Piga, Loris Roveda, 2021, Journal of Intelligent & Robotic Systems.

Fast predictive visual servoing: A reference governor-based approach

E. Hoffman, D. Piga, Antonio Paolillo, 2023, Control Engineering Practice.

Sparse Optimization for Automated Energy End Use Disaggregation

Andrea Castelletti, Andrea Emilio Rizzoli, Matteo Giuliani, 2016, IEEE Transactions on Control Systems Technology.

Learning Dynamical Systems From Quantized Observations: A Bayesian Perspective

D. Piga, Manas Mejari, Marco Forgione, 2022, IEEE Transactions on Automatic Control.

Robot control parameters auto-tuning in trajectory tracking applications

Marco Forgione, Dario Piga, Loris Roveda, 2020 .

Sensorless Optimal Interaction Control Exploiting Environment Stiffness Estimation

Dario Piga, Niccolò Iannacci, Loris Roveda, 2021, IEEE Transactions on Control Systems Technology.

Predicting clinical outcomes in the Machine Learning era: The Piacenza score a purely data driven approach for mortality prediction in COVID-19 Pneumonia

M. Piepoli, D. Piga, M. Deriu, 2021, medRxiv.

Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks

Asad Ali Shahid, A. Bemporad, D. Piga, 2021, IEEE Robotics and Automation Letters.

Optimal physical human–robot collaborative controller with user-centric tuning

D. Piga, F. Braghin, L. Roveda, 2023, Control Engineering Practice.

Q-Learning-Based Model Predictive Variable Impedance Control for Physical Human-Robot Collaboration (Extended Abstract)

Asad Ali Shahid, D. Piga, F. Braghin, 2023, International Joint Conference on Artificial Intelligence.

Human Preferences' Optimization in pHRI Collaborative Tasks

M. Beschi, D. Piga, L. Roveda, 2023, 2023 20th International Conference on Ubiquitous Robots (UR).

A Human-Centric Framework for Robotic Task Learning and Optimization

A. Ajoudani, D. Piga, L. Roveda, 2023, SSRN Electronic Journal.

Q-Learning-based model predictive variable impedance control for physical human-robot collaboration

Asad Ali Shahid, D. Piga, F. Braghin, 2022, Artif. Intell..

Preference-Based Optimization of a Human-Robot Collaborative Controller

D. Piga, F. Braghin, L. Roveda, 2022, IFAC-PapersOnLine.

Preference-based MPC calibration

Alberto Bemporad, Dario Piga, Mengjia Zhu, 2020, 2021 European Control Conference (ECC).

Robot joint friction compensation learning enhanced by 6D virtual sensor

D. Piga, F. Braghin, L. Roveda, 2022, International Journal of Robust and Nonlinear Control.

Robust state dependent Riccati equation variable impedance control for robotic force-tracking tasks

Loris Roveda, Dario Piga, D. Piga, 2020, International Journal of Intelligent Robotics and Applications.

6D Virtual Sensor for Wrench Estimation in Robotized Interaction Tasks Exploiting Extended Kalman Filter

Francesco Braghin, Dario Piga, Loris Roveda, 2020, Machines.

dynoNet: A neural network architecture for learning dynamical systems pdf

Marco Forgione, Dario Piga, D. Piga, 2020, International Journal of Adaptive Control and Signal Processing.

Towards direct data-driven model-free design of optimal controllers

Alberto Bemporad, Daniela Selvi, Dario Piga, 2018, 2018 European Control Conference (ECC).

Torque Vectoring for High-Performance Electric Vehicles: An Efficient MPC Calibration

Matteo Corno, Simone Formentin, Dario Piga, 2020, IEEE Control Systems Letters.

Optimal direct data-driven control with stability guarantees

Giorgio Battistelli, Alberto Bemporad, Daniela Selvi, 2020, Eur. J. Control.

Maximum-a-posteriori estimation of LTI state-space models via efﬁcient Monte-Carlo sampling

D. Piga, Manas Mejari, 2023 .

Maximum—A Posteriori Estimation of Linear Time-Invariant State-Space Models via Efficient Monte-Carlo Sampling

D. Piga, Manas Mejari, 2021, ASME Letters in Dynamic Systems and Control.

A bias-correction method for closed-loop identification of Linear Parameter-Varying systems

Alberto Bemporad, Dario Piga, Manas Mejari, 2018, Autom..

Direct learning of LPV controllers from data

Sergio M. Savaresi, Simone Formentin, Roland Tóth, 2016, Autom..

An instrumental least squares support vector machine for nonlinear system identification

Wei Xing Zheng, Roland Tóth, Dario Piga, 2013, Autom..

Control Parameters Tuning Based on Bayesian Optimization for Robot Trajectory Tracking

Marco Forgione, Dario Piga, Loris Roveda, 2019 .

Optimization of airborne wind energy generators

Dario Piga, Lorenzo Fagiano, Mario Milanese, 2012 .

Control as a key technology for a radical innovation in wind energy generation

L Fagiano, M Milanese, D Piga, 2010, Proceedings of the 2010 American Control Conference.

Kitenergy: a radical innovation in wind energy generation

Lorenzo Fagiano, Mario Milanese, Dario Piga, 2009 .

Semialgebraic Outer Approximations for Set-Valued Nonlinear Filtering

Alessio Benavoli, Dario Piga, D. Piga, 2019, 2019 18th European Control Conference (ECC).

A Unified Framework for Solving a General Class of Conditional and Robust Set-Membership Estimation Problems

J. Lasserre, V. Cerone, D. Regruto, 2014, IEEE Transactions on Automatic Control.

Set-Membership Error-in-Variables Identification Through Convex Relaxation Techniques

Dario Piga, Vito Cerone, Diego Regruto, 2012, IEEE Transactions on Automatic Control.

Set-membership identification of Hammerstein-Wiener systems

Dario Piga, Vito Cerone, Diego Regruto, 2011, IEEE Conference on Decision and Control and European Control Conference.

Hammerstein systems parameters bounding through sparse polynomial optimization

V. Cerone, D. Regruto, D. Piga, 2011, Proceedings of the 2011 American Control Conference.

Improved parameter bounds for set‐membership EIV problems

Dario Piga, Vito Cerone, Diego Regruto, 2011 .

Set-membership EIV identification through LMI relaxation techniques

V. Cerone, D. Regruto, D. Piga, 2010, Proceedings of the 2010 American Control Conference.

Set-membership identification of block-structured nonlinear feedback systems

Dario Piga, Vito Cerone, Diego Regruto, 2009, Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference.

Continuous control actions learning and adaptation for robotic manipulation through reinforcement learning

Asad Ali Shahid, D. Piga, F. Braghin, 2022, Autonomous Robots.

In-context learning for model-free system identification

D. Piga, Marco Forgione, F. Pura, 2023, ArXiv.

On the adaptation of recurrent neural networks for system identification

Marco Forgione, Dario Piga, Marco Gallieri, 2022, Autom..

Deep learning with transfer functions: new applications in system identification pdf

Marco Forgione, Dario Piga, Manas Mejari, 2021, IFAC-PapersOnLine.

Online end-use energy disaggregation via jump linear models

Alberto Bemporad, Dario Piga, Valentina Breschi, 2019, Control Engineering Practice.

An SDP approach for l0-minimization: Application to ARX model segmentation

Roland Tóth, Dario Piga, R. Tóth, 2013, Autom..

Robust pole placement for plants with semialgebraic parametric uncertainty

Dario Piga, Vito Cerone, Diego Regruto, 2012, 2012 American Control Conference (ACC).

Pairwise Preferences-Based Optimization of a Path-Based Velocity Planner in Robotic Sealing Tasks

Asad Ali Shahid, A. Bemporad, D. Piga, 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

Convex relaxation techniques for set-membership identification of LPV systems

V. Cerone, D. Regruto, D. Piga, 2011, Proceedings of the 2011 American Control Conference.

Bayesian Optimization For Choice Data

D. Piga, A. Benavoli, Dario Azzimonti, 2023, GECCO Companion.

Shrinking complexity of scheduling dependencies in LS-SVM based LPV system identification

Roland Tóth, Dario Piga, Vincent Laurain, 2014, 53rd IEEE Conference on Decision and Control.

LPV model order selection in an LS-SVM setting

Roland Tóth, Dario Piga, R. Tóth, 2013, 52nd IEEE Conference on Decision and Control.