Publications
1.
Bertozzi, Andrea; Vergara, Claudio; Reuchlin, Sweder; Schmehl, Roland
Pumping cycle optimization of soft-wing airborne wind energy systems using quasi-steady simulations Conference
TORQUE 2026, vol. 3224, no. 9, Journal of Physics: Conference Series IOP Publishing, 2026.
@conference{Bertozzi2026a,
title = {Pumping cycle optimization of soft-wing airborne wind energy systems using quasi-steady simulations},
author = {Andrea Bertozzi and Claudio Vergara and Sweder Reuchlin and Roland Schmehl},
doi = {10.1088/1742-6596/3224/9/092017},
year = {2026},
date = {2026-05-01},
urldate = {2026-05-01},
booktitle = {TORQUE 2026},
journal = {Journal of Physics: Conference Series},
volume = {3224},
number = {9},
pages = {092017},
publisher = {IOP Publishing},
series = {Journal of Physics: Conference Series},
abstract = {This study presents a quasi-steady simulation and optimization framework for ground-generation airborne wind energy systems with soft kites. A novel parameterization of the reel-out phase is introduced, explicitly resolving crosswind maneuvers through a kinematically feasible figure-of-eight trajectory, thereby improving physical representativeness compared to conventional averaged crosswind models. The framework is validated against experimental data from the TU Delft V3 reference kite, showing good agreement in kite kinematics, phase- and cycle-averaged power, while highlighting expected limitations in instantaneous force prediction. The model is subsequently used to optimize pumping-cycle operational parameters under fixed environmental conditions, revealing significant variations in cycle duration and modest gains in average power. The results demonstrate the suitability of the proposed framework for efficient conceptual design studies and operational optimization of airborne wind energy systems.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This study presents a quasi-steady simulation and optimization framework for ground-generation airborne wind energy systems with soft kites. A novel parameterization of the reel-out phase is introduced, explicitly resolving crosswind maneuvers through a kinematically feasible figure-of-eight trajectory, thereby improving physical representativeness compared to conventional averaged crosswind models. The framework is validated against experimental data from the TU Delft V3 reference kite, showing good agreement in kite kinematics, phase- and cycle-averaged power, while highlighting expected limitations in instantaneous force prediction. The model is subsequently used to optimize pumping-cycle operational parameters under fixed environmental conditions, revealing significant variations in cycle duration and modest gains in average power. The results demonstrate the suitability of the proposed framework for efficient conceptual design studies and operational optimization of airborne wind energy systems.
2.
Bertozzi, Andrea; Paduano, Bruno; Niosi, Francesco; Alborghetti, Mattia; Fagiano, Lorenzo; Bracco, Giovanni
Numerical analysis of a floating Airborne Wind Energy farm with a shared-mooring system Journal Article
In: Applied Ocean Research, vol. 174, pp. 105215, 2026, ISSN: 0141-1187.
@article{Bertozzi2026b,
title = {Numerical analysis of a floating Airborne Wind Energy farm with a shared-mooring system},
author = {Andrea Bertozzi and Bruno Paduano and Francesco Niosi and Mattia Alborghetti and Lorenzo Fagiano and Giovanni Bracco},
doi = {https://doi.org/10.1016/j.apor.2026.105215},
issn = {0141-1187},
year = {2026},
date = {2026-01-01},
urldate = {2026-01-01},
journal = {Applied Ocean Research},
volume = {174},
pages = {105215},
abstract = {Airborne wind energy (AWE) has the potential to complement conventional wind power and accelerate the transition to renewable energy. Realizing this potential will require offshore farm-scale deployments, where mooring systems represent a significant share of capital expenditures. Shared-line mooring concepts have been proposed to reduce these costs; however, floating AWE farms have not yet been studied, and shared-line designs have not yet been commercialized due to the complexity of mooring-induced platform-to–platform interactions. At the same time, taut mooring systems employing synthetic ropes are gaining interest, yet their inherently nonlinear behaviour is often neglected despite its known influence on system dynamics. This study addresses these gaps by numerically investigating the role of nonlinear tensile stiffness in shared-line moorings for floating AWE systems. A fully nonlinear stiffness model, based on industrial rope characteristics, is integrated to capture realistic material behaviour. The dynamics of a nine-platform shared-line farm are compared against a baseline single-device configuration under critical loading conditions, evaluating motions, line tensions, and hourly fatigue damage accumulation rate. Results show that even simple single-mooring configurations experience strong dynamic effects from material nonlinearities, including notable resonance shifts relative to linearized models. In shared-line arrangements, high-frequency load components associated with higher-order harmonics significantly accelerate fatigue damage accumulation. These findings underscore the critical importance of nonlinear mooring modelling in the design and assessment of shared-moored floating renewable energy systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Airborne wind energy (AWE) has the potential to complement conventional wind power and accelerate the transition to renewable energy. Realizing this potential will require offshore farm-scale deployments, where mooring systems represent a significant share of capital expenditures. Shared-line mooring concepts have been proposed to reduce these costs; however, floating AWE farms have not yet been studied, and shared-line designs have not yet been commercialized due to the complexity of mooring-induced platform-to–platform interactions. At the same time, taut mooring systems employing synthetic ropes are gaining interest, yet their inherently nonlinear behaviour is often neglected despite its known influence on system dynamics. This study addresses these gaps by numerically investigating the role of nonlinear tensile stiffness in shared-line moorings for floating AWE systems. A fully nonlinear stiffness model, based on industrial rope characteristics, is integrated to capture realistic material behaviour. The dynamics of a nine-platform shared-line farm are compared against a baseline single-device configuration under critical loading conditions, evaluating motions, line tensions, and hourly fatigue damage accumulation rate. Results show that even simple single-mooring configurations experience strong dynamic effects from material nonlinearities, including notable resonance shifts relative to linearized models. In shared-line arrangements, high-frequency load components associated with higher-order harmonics significantly accelerate fatigue damage accumulation. These findings underscore the critical importance of nonlinear mooring modelling in the design and assessment of shared-moored floating renewable energy systems.
3.
Bertozzi, Andrea; Niosi, Francesco; Dell’Edera, Oronzo; Paduano, Bruno; Bracco, Giovanni
Innovative calibration procedure of numerical models for FOWTs: an experimental validation Conference
EERA DeepWind 2025, vol. 3131, no. 1, Journal of Physics: Conference Series IOP Publishing, 2025.
@conference{Bertozzi2025,
title = {Innovative calibration procedure of numerical models for FOWTs: an experimental validation},
author = {Andrea Bertozzi and Francesco Niosi and Oronzo Dell’Edera and Bruno Paduano and Giovanni Bracco},
doi = {10.1088/1742-6596/3131/1/012009},
year = {2025},
date = {2025-10-01},
urldate = {2025-10-01},
booktitle = {EERA DeepWind 2025},
journal = {Journal of Physics: Conference Series},
volume = {3131},
number = {1},
pages = {012009},
publisher = {IOP Publishing},
series = {Journal of Physics: Conference Series},
abstract = {Fine tuning of numerical models of Floating Offshore Wind Turbine (FOWT) dynamics is crucial to increase their response accuracy with respect to experimental data but still remains a significant challenge due to the large number of parameters involved. This paper builds on the results from a recent experimental campaign conducted on a 1:96 Froude-scaled model of the DeepCwind semi-submersible platform with a taut mooring system, presenting the development and calibration of a numerical model, focusing on the platform motion response. A two-stage optimization algorithm is employed to estimate linear and quadratic global damping coefficients exclusively using response data from free decay tests. Three operational sea-state conditions and one extreme condition are simulated leveraging the calibrated model, and results compared against experimental findings for validation. This study offers an efficient and widely applicable methodology for enhancing numerical modeling of FOWT systems.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Fine tuning of numerical models of Floating Offshore Wind Turbine (FOWT) dynamics is crucial to increase their response accuracy with respect to experimental data but still remains a significant challenge due to the large number of parameters involved. This paper builds on the results from a recent experimental campaign conducted on a 1:96 Froude-scaled model of the DeepCwind semi-submersible platform with a taut mooring system, presenting the development and calibration of a numerical model, focusing on the platform motion response. A two-stage optimization algorithm is employed to estimate linear and quadratic global damping coefficients exclusively using response data from free decay tests. Three operational sea-state conditions and one extreme condition are simulated leveraging the calibrated model, and results compared against experimental findings for validation. This study offers an efficient and widely applicable methodology for enhancing numerical modeling of FOWT systems.
4.
Alborghetti, Mattia; Trevisi, Filippo; Boffadossi, Roberto; Fagiano, Lorenzo
European Control Conference 2025, 2025.
@conference{alborghetti2025,
title = {Optimal Power Smoothing of Airborne Wind Energy Systems Via Pseudo-Spectral Methods and Multi-Objective Analysis},
author = {Mattia Alborghetti and Filippo Trevisi and Roberto Boffadossi and Lorenzo Fagiano},
url = {https://www.sas-lab.deib.polimi.it/?attachment_id=1544},
doi = {to appear},
year = {2025},
date = {2025-06-27},
urldate = {2025-06-27},
booktitle = {European Control Conference 2025},
abstract = {The problem of optimizing the power output of a class of Airborne Wind Energy Systems (AWES), named fly-gen, is considered. Fly-gen AWES, called windplanes in this paper, harvest wind power by means of an autonomous tethered aircraft that carries out periodic trajectories roughly perpendicular to the wind flow (crosswind conditions), using on-board turbines and converters and an electric tether to transfer power to the ground. The amount of generated power and its variability strongly depend on the flown trajectory, whose optimization is a highly nonlinear and non-convex problem. Differently from most of the existing literature on the topic, this problem is here addressed from a multi-objective perspective, where both the average power and its variability are considered. Through a recently-proposed pseudo-spectral decomposition of the states and inputs, a rather small-scale nonlinear program is derived to obtain a periodic orbit that maximizes the average power under a constraint on its variability. Then, a series of such programs is formulated and solved to approximate the Pareto front of the problem. Finally, the latter is exploited to analyze the possible trade-offs. The main finding of this work is that, contrary to what postulated so far in the scientific community, it is possible to operate the windplane with minimal power fluctuations (10% of the average) with a very small reduction of mean power, of the order of 5% with respect to the maximum achievable. Additional considerations regarding the sensitivity of the optimal trajectories to various factors are presented, too. These results pave the way for a completely novel way of optimizing and controlling windplanes.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The problem of optimizing the power output of a class of Airborne Wind Energy Systems (AWES), named fly-gen, is considered. Fly-gen AWES, called windplanes in this paper, harvest wind power by means of an autonomous tethered aircraft that carries out periodic trajectories roughly perpendicular to the wind flow (crosswind conditions), using on-board turbines and converters and an electric tether to transfer power to the ground. The amount of generated power and its variability strongly depend on the flown trajectory, whose optimization is a highly nonlinear and non-convex problem. Differently from most of the existing literature on the topic, this problem is here addressed from a multi-objective perspective, where both the average power and its variability are considered. Through a recently-proposed pseudo-spectral decomposition of the states and inputs, a rather small-scale nonlinear program is derived to obtain a periodic orbit that maximizes the average power under a constraint on its variability. Then, a series of such programs is formulated and solved to approximate the Pareto front of the problem. Finally, the latter is exploited to analyze the possible trade-offs. The main finding of this work is that, contrary to what postulated so far in the scientific community, it is possible to operate the windplane with minimal power fluctuations (10% of the average) with a very small reduction of mean power, of the order of 5% with respect to the maximum achievable. Additional considerations regarding the sensitivity of the optimal trajectories to various factors are presented, too. These results pave the way for a completely novel way of optimizing and controlling windplanes.
5.
Catenaro, Edoardo; Sabug, Lorenzo; Panzani, Giulio; Sette, Davide; Ruiz, Fredy; Fagiano, Lorenzo; Savaresi, Sergio M.
Automatic Learning-Based Calibration of Assisted Motorcycle Gearshift: A Comparative Study Journal Article
In: IEEE Transactions on Control Systems Technology, 2025.
@article{Catenaro2025,
title = {Automatic Learning-Based Calibration of Assisted Motorcycle Gearshift: A Comparative Study},
author = {Edoardo Catenaro and Lorenzo Sabug and Giulio Panzani and Davide Sette and Fredy Ruiz and Lorenzo Fagiano and Sergio M. Savaresi},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004672295&doi=10.1109%2fTCST.2025.3561504&partnerID=40&md5=9721bf3ba2794fb9cbd698537f8aaf75},
doi = {10.1109/TCST.2025.3561504},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IEEE Transactions on Control Systems Technology},
abstract = {A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from naïve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result. © 1993-2012 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A comparison of different approaches to the automatic online, data-driven calibration of assisted gearshift settings for a motorcycle is presented. An objective function associated with the component stress and clutch resynchronization time is exploited and optimized during operation using different strategies: from naïve space-filling approaches to learning-based black-box optimization algorithms. The performance of various methods is compared in real-world experiments using metrics related to the experimental convergence rate and the quality of the best found result. © 1993-2012 IEEE.
6.
Ahmed, Syed Hassan; Bonetti, Tommaso; Fagiano, Lorenzo
Periodic Disturbance Learning Model Predictive Control Journal Article
In: IEEE Control Systems Letters, 2025.
@article{Ahmed2025,
title = {Periodic Disturbance Learning Model Predictive Control},
author = {Syed Hassan Ahmed and Tommaso Bonetti and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105010219505&doi=10.1109%2fLCSYS.2025.3586633&partnerID=40&md5=ad42d331d4a119b26ac13d3387a4e55d},
doi = {10.1109/LCSYS.2025.3586633},
year = {2025},
date = {2025-01-01},
journal = {IEEE Control Systems Letters},
abstract = {A novel Model Predictive Control (MPC) framework called disturbance-learning MPC (DL-MPC) for constrained LTI systems subject to bounded disturbances is proposed. The primary objective is to improve the disturbance rejection performance of the tube-based MPC (tube-MPC) law, especially focusing on periodic disturbance signals. Based on convex optimization, the method uses real-time measurements to learn a model of the disturbance, to predict its future behavior. By including this model in the MPC, the latter can proactively counteract the disturbance, significantly improving closed-loop performance. The presented technique includes the disturbance model while preserving robust recursive feasibility and constraint satisfaction. The effectiveness of DL-MPC is demonstrated through simulation of a multivariable nonlinear system, a Continuous-flow Stirred Tank Reactor, subject to periodic disturbances. The results clearly show enhanced tracking accuracy compared to nominal MPC and tube-MPC methods. © 2017 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A novel Model Predictive Control (MPC) framework called disturbance-learning MPC (DL-MPC) for constrained LTI systems subject to bounded disturbances is proposed. The primary objective is to improve the disturbance rejection performance of the tube-based MPC (tube-MPC) law, especially focusing on periodic disturbance signals. Based on convex optimization, the method uses real-time measurements to learn a model of the disturbance, to predict its future behavior. By including this model in the MPC, the latter can proactively counteract the disturbance, significantly improving closed-loop performance. The presented technique includes the disturbance model while preserving robust recursive feasibility and constraint satisfaction. The effectiveness of DL-MPC is demonstrated through simulation of a multivariable nonlinear system, a Continuous-flow Stirred Tank Reactor, subject to periodic disturbances. The results clearly show enhanced tracking accuracy compared to nominal MPC and tube-MPC methods. © 2017 IEEE.
7.
Trevisi, Filippo; Sabug, Lorenzo; Fagiano, Lorenzo
A Gaussian wake model for Airborne Wind Energy Systems Conference
Wake Conference 2025 Visby, Sweden, vol. 3016, no. 1, Journal of Physics: Conference Series 2025.
@conference{Trevisi2025,
title = {A Gaussian wake model for Airborne Wind Energy Systems},
author = {Filippo Trevisi and Lorenzo Sabug and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-105007615616&doi=10.1088%2f1742-6596%2f3016%2f1%2f012038&partnerID=40&md5=e2dceed356557b4c5caec901a97faf58},
doi = {10.1088/1742-6596/3016/1/012038},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
booktitle = {Wake Conference 2025 Visby, Sweden},
journal = {Journal of Physics: Conference Series},
volume = {3016},
number = {1},
series = {Journal of Physics: Conference Series},
abstract = {Modeling the aerodynamic interactions between Airborne Wind Energy Systems (AWES) is an open and challenging problem. To this end, a new Gaussian wake model is introduced for fly-gen AWES, also called windplanes here. The engineering wake models of windplanes can be split in induction models, used to find the induced velocities at the wing, and wake models, used to account for the wind deficit downwind. The proposed model combines an improved induction model, starting from one available in the literature, with a novel wake model that assumes that the wake velocity has a Gaussian shape and imposes momentum conservation. The variance of the Gaussian wake is assumed to vary with the downstream distance from the windplane. The model entails just one tuning parameter that is estimated using CFD results from the literature, showing high accuracy. The new model is then used to study the influence of an upwind system on a downwind one. A sensitivity analysis is carried out by moving the downwind system in the direction transverse to the wind speed or by yawing it with respect to the wind direction. A maximum in power production of the downwind system is found when the projections of the two trajectories are partially overlapping. The extremely low computational cost of the model and the physical insights provided by this paper contribute to clear the way for simulation, planning and control of large scale airborne wind farms. © Published under licence by IOP Publishing Ltd.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Modeling the aerodynamic interactions between Airborne Wind Energy Systems (AWES) is an open and challenging problem. To this end, a new Gaussian wake model is introduced for fly-gen AWES, also called windplanes here. The engineering wake models of windplanes can be split in induction models, used to find the induced velocities at the wing, and wake models, used to account for the wind deficit downwind. The proposed model combines an improved induction model, starting from one available in the literature, with a novel wake model that assumes that the wake velocity has a Gaussian shape and imposes momentum conservation. The variance of the Gaussian wake is assumed to vary with the downstream distance from the windplane. The model entails just one tuning parameter that is estimated using CFD results from the literature, showing high accuracy. The new model is then used to study the influence of an upwind system on a downwind one. A sensitivity analysis is carried out by moving the downwind system in the direction transverse to the wind speed or by yawing it with respect to the wind direction. A maximum in power production of the downwind system is found when the projections of the two trajectories are partially overlapping. The extremely low computational cost of the model and the physical insights provided by this paper contribute to clear the way for simulation, planning and control of large scale airborne wind farms. © Published under licence by IOP Publishing Ltd.
8.
Kessler, Nicolas; Fagiano, Lorenzo
On Gain Scheduling Trajectory Stabilization for Nonlinear Systems: Theoretical Insights and Experimental Results Journal Article
In: International Journal of Robust and Nonlinear Control, vol. 35, no. 6, pp. 2142 – 2155, 2025, (All Open Access, Green Open Access, Hybrid Gold Open Access).
@article{Kessler20252142,
title = {On Gain Scheduling Trajectory Stabilization for Nonlinear Systems: Theoretical Insights and Experimental Results},
author = {Nicolas Kessler and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000427164&doi=10.1002%2frnc.7784&partnerID=40&md5=60657f166d42065468d94f1366f650bd},
doi = {10.1002/rnc.7784},
year = {2025},
date = {2025-01-01},
journal = {International Journal of Robust and Nonlinear Control},
volume = {35},
number = {6},
pages = {2142 – 2155},
abstract = {Steering a nonlinear system from an initial state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example, via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees bounded deviations around the found trajectory can be much more involved. An approach that does not require high online computational power and is well-accepted in industry is gain-scheduling. The results presented here pertain to the boundedness guarantees and the set of safe initial conditions of gain-scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time-varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions for the existence of a robust polyquadratic Lyapunov function to attempt the derivation of the desired gain-scheduled controller via the solution of linear matrix inequalities (LMIs). A result to estimate an ellipsoidal set of safe initial conditions is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can also be used to check/assess the boundedness properties obtained with an existing gain-scheduled law. The approach is demonstrated experimentally on a small quadcopter, as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model. © 2025 The Author(s). International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd.},
note = {All Open Access, Green Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Steering a nonlinear system from an initial state to a desired one is a common task in control. While a nominal trajectory can be obtained rather systematically using a model, for example, via numerical optimization, heuristics, or reinforcement learning, the design of a computationally fast and reliable feedback control law that guarantees bounded deviations around the found trajectory can be much more involved. An approach that does not require high online computational power and is well-accepted in industry is gain-scheduling. The results presented here pertain to the boundedness guarantees and the set of safe initial conditions of gain-scheduled control laws, based on subsequent linearizations along the reference trajectory. The approach bounds the uncertainty arising from the linearization process, builds polytopic sets of linear time-varying systems covering the nonlinear dynamics along the trajectory, and exploits sufficient conditions for the existence of a robust polyquadratic Lyapunov function to attempt the derivation of the desired gain-scheduled controller via the solution of linear matrix inequalities (LMIs). A result to estimate an ellipsoidal set of safe initial conditions is provided too. Moreover, arbitrary scheduling strategies between the control gains are considered in the analysis, and the method can also be used to check/assess the boundedness properties obtained with an existing gain-scheduled law. The approach is demonstrated experimentally on a small quadcopter, as well as in simulation to design a scheduled controller for a chemical reactor model and to validate an existing control law for a gantry crane model. © 2025 The Author(s). International Journal of Robust and Nonlinear Control published by John Wiley & Sons Ltd.
9.
Bordignon, Matteo; Croce, Alessandro; Fagiano, Lorenzo Mario
On Wind Estimation Techniques for Airborne Wind Energy Systems Conference
vol. 59, no. 30, 2025, ISSN: 2405-8963, (5th Conference on Modeling, Estimation and Control MECC 2025).
@conference{BORDIGNON2025191,
title = {On Wind Estimation Techniques for Airborne Wind Energy Systems},
author = {Matteo Bordignon and Alessandro Croce and Lorenzo Mario Fagiano},
url = {https://www.sciencedirect.com/science/article/pii/S2405896325029453},
doi = {https://doi.org/10.1016/j.ifacol.2025.12.235},
issn = {2405-8963},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {IFAC-PapersOnLine},
volume = {59},
number = {30},
pages = {191-196},
abstract = {Airborne Wind Energy (AWE) exploits kites for high-altitude power generation but faces control challenges due to system complexity and wind uncertainty. Accurate wind estimation at operational altitude is essential, yet current methods like extrapolation and EKF present significant drawbacks. This work proposes two novel, generalizable estimation techniques requiring only minimal sensor data (kite position, tether force). One employs optimization on a simplified model, while the second solves a linear system derived from specific dynamic assumptions, suitable for least-squares or Kalman filtering. Both methods are tested using real flight data and compared with surface wind speed measurements.},
note = {5th Conference on Modeling, Estimation and Control MECC 2025},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Airborne Wind Energy (AWE) exploits kites for high-altitude power generation but faces control challenges due to system complexity and wind uncertainty. Accurate wind estimation at operational altitude is essential, yet current methods like extrapolation and EKF present significant drawbacks. This work proposes two novel, generalizable estimation techniques requiring only minimal sensor data (kite position, tether force). One employs optimization on a simplified model, while the second solves a linear system derived from specific dynamic assumptions, suitable for least-squares or Kalman filtering. Both methods are tested using real flight data and compared with surface wind speed measurements.
10.
Wang, Lu; Robertson, Amy; Jonkman, Jason; Liao, Yingqian; Berthelsen, Petter Andreas; Abdelmoteleb, Serag-Eldin; Rohrer, Peter; Rajasree, Vishnu Ramachandran Nair; Bachynski-Polić, Erin; Clement, Constance; Cunff, Cédric Le; Vlachogiannis, Prokopios; Peyrard, Christophe; Pham, Dam Thanh; Leahy, Paul; Bouysses, Valérie; Brun, Cédric; Wang, Lizhong; Wang, Lilin; Teng, Long; Shi, Wei; Fu, Yushun; Abid, Ali; Cheng, Zhengshun; Chen, Peng; Hu, Zhirong; Bai, Haozhe; Xu, Kun; Das, Tuhin; Sarker, Doyal; Ngo, Tri; Casal, Pau Trubat; Molins, Climent; Niosi, Francesco; Dell'Edera, Oronzo; Paduano, Bruno; Bertozzi, Andrea; Bracco, Giovanni; Tagliafierro, Bonaventura; Moghtadaei, Abdolmajid; Wright, Christopher
OC7 phase I: Toward practical sea-state-dependent modeling of hydrodynamic viscous drag and damping Journal Article
In: Ocean Engineering, vol. 336, pp. 121745, 2025, ISSN: 0029-8018.
@article{Wang2025,
title = {OC7 phase I: Toward practical sea-state-dependent modeling of hydrodynamic viscous drag and damping},
author = {Lu Wang and Amy Robertson and Jason Jonkman and Yingqian Liao and Petter Andreas Berthelsen and Serag-Eldin Abdelmoteleb and Peter Rohrer and Vishnu Ramachandran Nair Rajasree and Erin Bachynski-Polić and Constance Clement and Cédric Le Cunff and Prokopios Vlachogiannis and Christophe Peyrard and Dam Thanh Pham and Paul Leahy and Valérie Bouysses and Cédric Brun and Lizhong Wang and Lilin Wang and Long Teng and Wei Shi and Yushun Fu and Ali Abid and Zhengshun Cheng and Peng Chen and Zhirong Hu and Haozhe Bai and Kun Xu and Tuhin Das and Doyal Sarker and Tri Ngo and Pau Trubat Casal and Climent Molins and Francesco Niosi and Oronzo Dell'Edera and Bruno Paduano and Andrea Bertozzi and Giovanni Bracco and Bonaventura Tagliafierro and Abdolmajid Moghtadaei and Christopher Wright},
doi = {https://doi.org/10.1016/j.oceaneng.2025.121745},
issn = {0029-8018},
year = {2025},
date = {2025-01-01},
urldate = {2025-01-01},
journal = {Ocean Engineering},
volume = {336},
pages = {121745},
abstract = {This article presents a collaborative research campaign under the OC7 project on refining the engineering modeling approach for hydrodynamic viscous drag and damping on floating wind platforms, focusing on the adjustment of hydrodynamic drag and damping coefficients for different sea states. The participant simulation results show significant improvements over the previous OC6 project in predicting the low-frequency resonance motion under nonoperational conditions. The improvements are mainly due to enhanced modeling, including the adoption of wave stretching, and directly tuning the coefficients to measured platform motion in waves instead of free decay. For accurate predictions of mean- and slow-drift motion, the better performing models use a decreasing column splash zone drag coefficient and increasing surge damping/drag with increasing wave height. The model tuning for heave and pitch resonance shows less consistency. Generally, both quadratic drag and additional heave or pitch damping are needed for accurate predictions. Alternatively, a quadratic drag formulation with velocity filtering for the rectangular pontoons leads to improved predictions without additional damping. This model is also potentially more predictive, requiring minimal adjustment to its parameters for different conditions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This article presents a collaborative research campaign under the OC7 project on refining the engineering modeling approach for hydrodynamic viscous drag and damping on floating wind platforms, focusing on the adjustment of hydrodynamic drag and damping coefficients for different sea states. The participant simulation results show significant improvements over the previous OC6 project in predicting the low-frequency resonance motion under nonoperational conditions. The improvements are mainly due to enhanced modeling, including the adoption of wave stretching, and directly tuning the coefficients to measured platform motion in waves instead of free decay. For accurate predictions of mean- and slow-drift motion, the better performing models use a decreasing column splash zone drag coefficient and increasing surge damping/drag with increasing wave height. The model tuning for heave and pitch resonance shows less consistency. Generally, both quadratic drag and additional heave or pitch damping are needed for accurate predictions. Alternatively, a quadratic drag formulation with velocity filtering for the rectangular pontoons leads to improved predictions without additional damping. This model is also potentially more predictive, requiring minimal adjustment to its parameters for different conditions.
11.
Meza, Gonzalo; Lowenstein, Kristoffer Fink; Fagiano, Lorenzo
Obstacle avoidance for a robotic manipulator with linear-quadratic Model Predictive Control Conference
2024.
@conference{Meza20243365,
title = {Obstacle avoidance for a robotic manipulator with linear-quadratic Model Predictive Control},
author = {Gonzalo Meza and Kristoffer Fink Lowenstein and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208276051&doi=10.1109%2fCASE59546.2024.10711546&partnerID=40&md5=dc5df15c31ee283e5083ebcec6b1e560},
doi = {10.1109/CASE59546.2024.10711546},
year = {2024},
date = {2024-01-01},
journal = {IEEE International Conference on Automation Science and Engineering},
pages = {3365 – 3370},
abstract = {The problem of moving a six-degrees-of-freedom manipulator in an environment with unknown obstacles is considered. The manipulator is assumed to be equipped with an exteroceptive sensor that provides a partial sampling of the surroundings. A hierarchical control layout is proposed: in the outer layer, a path planner generates an obstacle-free trajectory based on the available local information; in the inner layer, an Inverse-Kinematics Model Predictive Controller tracks the trajectory while reactively avoiding unseen obstacles and self-collisions at a higher rate. By constructing a polytopic under-approximation of the free environment and employing a linearization of the task, the predictive controller features a convex quadratic cost and linear constraints, thus requiring the solution of a quadratic program at each time step. The proposed method is evaluated on the kinematic model of a MyCobot280 robotic arm, showing the potential for real-time feasibility. © 2024 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The problem of moving a six-degrees-of-freedom manipulator in an environment with unknown obstacles is considered. The manipulator is assumed to be equipped with an exteroceptive sensor that provides a partial sampling of the surroundings. A hierarchical control layout is proposed: in the outer layer, a path planner generates an obstacle-free trajectory based on the available local information; in the inner layer, an Inverse-Kinematics Model Predictive Controller tracks the trajectory while reactively avoiding unseen obstacles and self-collisions at a higher rate. By constructing a polytopic under-approximation of the free environment and employing a linearization of the task, the predictive controller features a convex quadratic cost and linear constraints, thus requiring the solution of a quadratic program at each time step. The proposed method is evaluated on the kinematic model of a MyCobot280 robotic arm, showing the potential for real-time feasibility. © 2024 IEEE.
12.
Mohammed, Tareg; Busk, Jørgen; Oland, Espen; Fagiano, Lorenzo
Large-Scale Reverse Pumping for Rigid-Wing Airborne Wind Energy Systems Journal Article
In: Journal of Guidance, Control, and Dynamics, vol. 47, no. 8, pp. 1748 – 1758, 2024.
@article{Mohammed20241748,
title = {Large-Scale Reverse Pumping for Rigid-Wing Airborne Wind Energy Systems},
author = {Tareg Mohammed and Jørgen Busk and Espen Oland and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85201687286&doi=10.2514%2f1.G007859&partnerID=40&md5=022d3ef14b7aa30dcd28668b0b7a4c6e},
doi = {10.2514/1.G007859},
year = {2024},
date = {2024-01-01},
journal = {Journal of Guidance, Control, and Dynamics},
volume = {47},
number = {8},
pages = {1748 – 1758},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
13.
Sabug, Lorenzo; Ruiz, Fredy; Fagiano, Lorenzo
Multi-Agent Global Optimization with Decision Variable Coupling Conference
2024.
@conference{Sabug20242544,
title = {Multi-Agent Global Optimization with Decision Variable Coupling},
author = {Lorenzo Sabug and Fredy Ruiz and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-86000520212&doi=10.1109%2fCDC56724.2024.10886148&partnerID=40&md5=22a6fbefd438993a678fbc1fb7c8d063},
doi = {10.1109/CDC56724.2024.10886148},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of the IEEE Conference on Decision and Control},
pages = {2544 – 2550},
abstract = {A cooperative, multi-agent global optimization problem is considered, where the global cost function is the sum of the agents' private, non-convex costs. In contrast to all previously considered setups, evaluating the private costs involves a global experiment, using a common instance of the decision vector. This is relevant when each agent can only control a part ('subvariable') of the decision vector, but its private cost is also affected by the other subvariables. A novel cooperative optimization method using Set Membership identification and consensus-based techniques is proposed, to make all agents agree on the next global decision vector to be tested. A trade-off between exploitation close to the best point found and exploration around the search set is achieved, even without explicitly sharing the private costs' information. Statistical tests show that the proposed distributed method is competitive with respect to a centralized one. © 2024 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
A cooperative, multi-agent global optimization problem is considered, where the global cost function is the sum of the agents' private, non-convex costs. In contrast to all previously considered setups, evaluating the private costs involves a global experiment, using a common instance of the decision vector. This is relevant when each agent can only control a part ('subvariable') of the decision vector, but its private cost is also affected by the other subvariables. A novel cooperative optimization method using Set Membership identification and consensus-based techniques is proposed, to make all agents agree on the next global decision vector to be tested. A trade-off between exploitation close to the best point found and exploration around the search set is achieved, even without explicitly sharing the private costs' information. Statistical tests show that the proposed distributed method is competitive with respect to a centralized one. © 2024 IEEE.
14.
Trombini, Sofia; Pasta, Edoardo; Fagiano, Lorenzo
On the kite-platform interactions in offshore Airborne Wind Energy Systems: Frequency analysis and control approach Journal Article
In: European Journal of Control, vol. 80, 2024, (All Open Access, Green Open Access, Hybrid Gold Open Access).
@article{Trombini2024,
title = {On the kite-platform interactions in offshore Airborne Wind Energy Systems: Frequency analysis and control approach},
author = {Sofia Trombini and Edoardo Pasta and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196822443&doi=10.1016%2fj.ejcon.2024.101065&partnerID=40&md5=662a7b724fd8ae32429e343613eaa49d},
doi = {10.1016/j.ejcon.2024.101065},
year = {2024},
date = {2024-01-01},
journal = {European Journal of Control},
volume = {80},
abstract = {This study investigates deep offshore, pumping Airborne Wind Energy systems, focusing on the kite-platform interaction. The considered system includes a 360 m2 soft-wing kite, connected by a tether to a winch installed on a 10-meter-deep spar with four mooring lines. Wind power is converted into electricity with a feedback controlled periodic trajectory of the kite and corresponding reeling motion of the tether. An analysis of the mutual influence between the platform and the kite dynamics, with different wave regimes, reveals a rather small sensitivity of the flight pattern to the platform oscillations; on the other hand, the frequency of tether force oscillations can be close to the platform resonance peaks, resulting in possible increased fatigue loads and damage of the floating and submerged components. A control design procedure is then proposed to avoid this problem, acting on the kite path planner. Simulation results confirm the effectiveness of the approach. © 2024 The Authors},
note = {All Open Access, Green Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study investigates deep offshore, pumping Airborne Wind Energy systems, focusing on the kite-platform interaction. The considered system includes a 360 m2 soft-wing kite, connected by a tether to a winch installed on a 10-meter-deep spar with four mooring lines. Wind power is converted into electricity with a feedback controlled periodic trajectory of the kite and corresponding reeling motion of the tether. An analysis of the mutual influence between the platform and the kite dynamics, with different wave regimes, reveals a rather small sensitivity of the flight pattern to the platform oscillations; on the other hand, the frequency of tether force oscillations can be close to the platform resonance peaks, resulting in possible increased fatigue loads and damage of the floating and submerged components. A control design procedure is then proposed to avoid this problem, acting on the kite path planner. Simulation results confirm the effectiveness of the approach. © 2024 The Authors
15.
Sabug, Lorenzo; Fagiano, Lorenzo; Ruiz, Fredy
Sample-Based Trust Region Dynamics in Contextual Global Optimization Journal Article
In: IEEE Control Systems Letters, vol. 8, pp. 1619 – 1624, 2024, (All Open Access, Hybrid Gold Open Access).
@article{Sabug20241619,
title = {Sample-Based Trust Region Dynamics in Contextual Global Optimization},
author = {Lorenzo Sabug and Lorenzo Fagiano and Fredy Ruiz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196083674&doi=10.1109%2fLCSYS.2024.3414970&partnerID=40&md5=5e5d29a34d1c74fce70fc29f899a71bd},
doi = {10.1109/LCSYS.2024.3414970},
year = {2024},
date = {2024-01-01},
journal = {IEEE Control Systems Letters},
volume = {8},
pages = {1619 – 1624},
abstract = {The problem of contextual global optimization is treated, in which a generally non-convex scalar objective (possibly black-box) depends not only on the decision variables, but also on uncontrollable, observable context variables. Assuming Lipschitz continuity of the objective function with respect to its arguments, the proposed approach builds a Set Membership model from observed samples. According to the observed context, a submodel that relates the objective to the decision variables is isolated, and used by a zeroth-order technique to pick the appropriate decision variable for sampling. A novel trust region dynamic is introduced, adjusting its size with samples instead of iterations. Such a technique makes the resulting contextual optimization algorithm more flexible with respect to the context behavior, whether it is changing smoothly, abruptly, or a combination of both. Benchmark tests and a case study demonstrate the efficacy of the proposed method. © 2017 IEEE.},
note = {All Open Access, Hybrid Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The problem of contextual global optimization is treated, in which a generally non-convex scalar objective (possibly black-box) depends not only on the decision variables, but also on uncontrollable, observable context variables. Assuming Lipschitz continuity of the objective function with respect to its arguments, the proposed approach builds a Set Membership model from observed samples. According to the observed context, a submodel that relates the objective to the decision variables is isolated, and used by a zeroth-order technique to pick the appropriate decision variable for sampling. A novel trust region dynamic is introduced, adjusting its size with samples instead of iterations. Such a technique makes the resulting contextual optimization algorithm more flexible with respect to the context behavior, whether it is changing smoothly, abruptly, or a combination of both. Benchmark tests and a case study demonstrate the efficacy of the proposed method. © 2017 IEEE.
16.
Kessler, Nicolas; Fagiano, Lorenzo
vol. 58, no. 18, 2024, (All Open Access, Gold Open Access).
@conference{Kessler2024263,
title = {On the Design of Terminal Ingredients for Linear Time Varying Model Predictive Control: Theory and Experimental Application},
author = {Nicolas Kessler and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206087125&doi=10.1016%2fj.ifacol.2024.09.041&partnerID=40&md5=e95b443facb45ff988c693a7dce1c01b},
doi = {10.1016/j.ifacol.2024.09.041},
year = {2024},
date = {2024-01-01},
journal = {IFAC-PapersOnLine},
volume = {58},
number = {18},
pages = {263 – 268},
abstract = {The use of Linear Time Varying (LTV) Model Predictive Control (MPC) to stabilize a set of trajectories of a nonlinear system is considered. This technique has been successfully applied in simulations and experiments, but only few contributions investigate stability aspects and the essential involved quantities: the terminal penalty and terminal constraint. Deriving the former is not always thoroughly addressed or it is based on the -rather restrictive- assumption that the whole set of linearized dynamics is quadratically stabilizable. In this article, we propose Linear Matrix Inequality (LMI) conditions to co-design a gain-scheduled auxiliary feedback and Lyapunov function, used to derive offline terminal set conditions and a terminal penalty constraint for an LTV MPC scheme guaranteeing stability and recursive constraint satisfaction. Recent results by the authors are extended to the case of a varying stage cost, such that the controller can be tuned to meet time-varying trade-offs between tracking accuracy and input activity. The approach is demonstrated in embedded hardware running on a CrazyFlie drone. Copyright © 2024 The Authors.},
note = {All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The use of Linear Time Varying (LTV) Model Predictive Control (MPC) to stabilize a set of trajectories of a nonlinear system is considered. This technique has been successfully applied in simulations and experiments, but only few contributions investigate stability aspects and the essential involved quantities: the terminal penalty and terminal constraint. Deriving the former is not always thoroughly addressed or it is based on the -rather restrictive- assumption that the whole set of linearized dynamics is quadratically stabilizable. In this article, we propose Linear Matrix Inequality (LMI) conditions to co-design a gain-scheduled auxiliary feedback and Lyapunov function, used to derive offline terminal set conditions and a terminal penalty constraint for an LTV MPC scheme guaranteeing stability and recursive constraint satisfaction. Recent results by the authors are extended to the case of a varying stage cost, such that the controller can be tuned to meet time-varying trade-offs between tracking accuracy and input activity. The approach is demonstrated in embedded hardware running on a CrazyFlie drone. Copyright © 2024 The Authors.
17.
Løwenstein, Kristoffer Fink; Bernardini, Daniele; Bemporad, Alberto; Fagiano, Lorenzo
vol. 58, no. 18, 2024, (All Open Access, Gold Open Access).
@conference{Løwenstein202478,
title = {Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural Networks in Gray-box Models},
author = {Kristoffer Fink Løwenstein and Daniele Bernardini and Alberto Bemporad and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206110971&doi=10.1016%2fj.ifacol.2024.09.013&partnerID=40&md5=7ccb836b7754ac684023e234fd253678},
doi = {10.1016/j.ifacol.2024.09.013},
year = {2024},
date = {2024-01-01},
journal = {IFAC-PapersOnLine},
volume = {58},
number = {18},
pages = {78 – 85},
abstract = {In Model Predictive Control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input-output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations. Copyright © 2024 The Authors.},
note = {All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In Model Predictive Control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input-output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations. Copyright © 2024 The Authors.
18.
Alborghetti, Mattia; Montecchio, Giulio; Sabug, Lorenzo; Fagiano, Lorenzo; Ruiz, Fredy
Controlling the Exploitation/Exploration Trade-Off in Global Optimization: A Set Membership Approach Conference
2024.
@conference{Alborghetti20242918,
title = {Controlling the Exploitation/Exploration Trade-Off in Global Optimization: A Set Membership Approach},
author = {Mattia Alborghetti and Giulio Montecchio and Lorenzo Sabug and Lorenzo Fagiano and Fredy Ruiz},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204500773&doi=10.23919%2fACC60939.2024.10644762&partnerID=40&md5=038d82e90119804327bf4fed9ae5971e},
doi = {10.23919/ACC60939.2024.10644762},
year = {2024},
date = {2024-01-01},
journal = {Proceedings of the American Control Conference},
pages = {2918 – 2923},
abstract = {Trading off exploration and exploitation is a crucial task in global (or black-box) optimization, to balance the search for better local optimizers with the refinement of already-found ones. Often, such a trade-off is not easily controlled by the user, as it depends non-trivially on the tuning parameters of the selected algorithm. A new concept is proposed here, where the share of exploitation moves over the total number of iterations is regulated by a feedback control law, to achieve a user-defined set-point. This concept is applied to the recently proposed Set Membership Global Optimization (SMGO) technique, resulting in a modified algorithm. Additional computational improvements are presented as well, and the resulting approach is extensively tested and compared with other methods. The statistical tests indicate that the new algorithm has better iteration-based optimization performance than the original one, at the same time shortening the computational times by around one order of magnitude. © 2024 AACC.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
Trading off exploration and exploitation is a crucial task in global (or black-box) optimization, to balance the search for better local optimizers with the refinement of already-found ones. Often, such a trade-off is not easily controlled by the user, as it depends non-trivially on the tuning parameters of the selected algorithm. A new concept is proposed here, where the share of exploitation moves over the total number of iterations is regulated by a feedback control law, to achieve a user-defined set-point. This concept is applied to the recently proposed Set Membership Global Optimization (SMGO) technique, resulting in a modified algorithm. Additional computational improvements are presented as well, and the resulting approach is extensively tested and compared with other methods. The statistical tests indicate that the new algorithm has better iteration-based optimization performance than the original one, at the same time shortening the computational times by around one order of magnitude. © 2024 AACC.
19.
Frigo, Luca; Lauricella, Marco; Fagiano, Lorenzo
Data-driven modeling and quality prediction of clinker production in a cement plant Conference
2024.
@conference{Frigo2024464,
title = {Data-driven modeling and quality prediction of clinker production in a cement plant},
author = {Luca Frigo and Marco Lauricella and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204793664&doi=10.1109%2fCCTA60707.2024.10666547&partnerID=40&md5=401135c620b4589c5bf15dfa6119cd8a},
doi = {10.1109/CCTA60707.2024.10666547},
year = {2024},
date = {2024-01-01},
journal = {2024 IEEE Conference on Control Technology and Applications, CCTA 2024},
pages = {464 – 470},
abstract = {The problem of deriving a model to predict the clinker quality in a cement production plant is considered. The process has a highly complex and nonlinear dynamic behavior, making physics-based first-principle modelling ineffective. A data-driven approach is thus proposed, to obtain an input-output model able to represent the overall system dynamics and to estimate the quality key performance indicators (KPIs). The approach combines a dynamic linear model, to estimate the evolution of the main process variables, with a static nonlinear regression model, to infer the clinker quality KPIs. The parameters of the dynamic model are estimated recursively on-line in a Moving Horizon Estimation fashion, to adapt to time-varying conditions such as the (unmeasured and uncertain) fuel mix. Real-world data collected on a European cement plant are used both to develop the approach and to test its effectiveness. © 2024 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The problem of deriving a model to predict the clinker quality in a cement production plant is considered. The process has a highly complex and nonlinear dynamic behavior, making physics-based first-principle modelling ineffective. A data-driven approach is thus proposed, to obtain an input-output model able to represent the overall system dynamics and to estimate the quality key performance indicators (KPIs). The approach combines a dynamic linear model, to estimate the evolution of the main process variables, with a static nonlinear regression model, to infer the clinker quality KPIs. The parameters of the dynamic model are estimated recursively on-line in a Moving Horizon Estimation fashion, to adapt to time-varying conditions such as the (unmeasured and uncertain) fuel mix. Real-world data collected on a European cement plant are used both to develop the approach and to test its effectiveness. © 2024 IEEE.
20.
Cecchin, Leonardo; Trachte, Adrian; Fagiano, Lorenzo; Diehl, Moritz
Real-time prediction of human-generated reference signals for advanced digging control Conference
2024.
@conference{Cecchin2024496,
title = {Real-time prediction of human-generated reference signals for advanced digging control},
author = {Leonardo Cecchin and Adrian Trachte and Lorenzo Fagiano and Moritz Diehl},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85208267527&doi=10.1109%2fCASE59546.2024.10711371&partnerID=40&md5=c1efd1a75d0e4a4314ec741dffa8d98d},
doi = {10.1109/CASE59546.2024.10711371},
year = {2024},
date = {2024-01-01},
journal = {IEEE International Conference on Automation Science and Engineering},
pages = {496 – 501},
abstract = {In the realm of excavator control, advanced techniques, such as Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback), proved to have great potential for enhancing efficiency and performance. These methods rely on the knowledge of future reference, which is often pre-defined, to optimize the system behavior as a function of it. However, this assumption fails in applications where a human operator chooses the reference at runtime, such as in the case of non-autonomous digging operations. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks. We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted. © 2024 IEEE.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In the realm of excavator control, advanced techniques, such as Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback), proved to have great potential for enhancing efficiency and performance. These methods rely on the knowledge of future reference, which is often pre-defined, to optimize the system behavior as a function of it. However, this assumption fails in applications where a human operator chooses the reference at runtime, such as in the case of non-autonomous digging operations. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks. We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted. © 2024 IEEE.