Publications
1.
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.
2.
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},
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.
3.
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.
4.
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.
5.
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.
6.
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.
7.
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}
}
8.
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.
9.
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
10.
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.
11.
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.
12.
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.
13.
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.
14.
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.
15.
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.
16.
Cupo, Alessandro; Cecchin, Leonardo; Demir, Ozan; Fagiano, Lorenzo
Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators Conference
vol. 58, no. 28, 2024, (All Open Access, Gold Open Access).
@conference{Cupo2024450,
title = {Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators},
author = {Alessandro Cupo and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85218012405&doi=10.1016%2fj.ifacol.2025.01.087&partnerID=40&md5=db01ed60ccb89f612cdb4e670e3d99b3},
doi = {10.1016/j.ifacol.2025.01.087},
year = {2024},
date = {2024-01-01},
journal = {IFAC-PapersOnLine},
volume = {58},
number = {28},
pages = {450 – 455},
abstract = {An optimal trajectory planning approach for hydraulic excavator arms is presented, where the goal is to create trajectories that trade-off energy consumption and completion time. We develop a physics-based model of the excavator, which describes both the dynamics and the hydraulic system's behavior. Further investigation of the Optimal Control Problem, used to create the trajectory, allows for discussion regarding the trade-off between power and time recovering a wide range of solutions based on the designer's choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path. © 2024 The Authors.},
note = {All Open Access, Gold Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
An optimal trajectory planning approach for hydraulic excavator arms is presented, where the goal is to create trajectories that trade-off energy consumption and completion time. We develop a physics-based model of the excavator, which describes both the dynamics and the hydraulic system's behavior. Further investigation of the Optimal Control Problem, used to create the trajectory, allows for discussion regarding the trade-off between power and time recovering a wide range of solutions based on the designer's choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path. © 2024 The Authors.
17.
Leonesio, Marco; Fagiano, Lorenzo
Scalable Approximate Optimization of Objective Functions Represented by Random Forests Conference
2024, (All Open Access, Green Open Access).
@conference{Leonesio2024494,
title = {Scalable Approximate Optimization of Objective Functions Represented by Random Forests},
author = {Marco Leonesio and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85198225941&doi=10.1109%2fMED61351.2024.10566222&partnerID=40&md5=18267e2690b0b795293f396febcf1460},
doi = {10.1109/MED61351.2024.10566222},
year = {2024},
date = {2024-01-01},
journal = {2024 32nd Mediterranean Conference on Control and Automation, MED 2024},
pages = {494 – 499},
abstract = {The problem of global optimization of an objective function represented by a Random Forest (RF) is considered. A method to obtain an approximate solution at low computational complexity is proposed, resorting to the inherent structure of an RF, which is a non-parametric model that partitions the feature space in convex polytopes according to the training data. The approach selects the optimal solution inside the polytopes corresponding to the best data points. It is shown that the proposed approximate method is significantly more efficient, thus applicable at large scale, than extensive global search algorithms, such as gridding and Mixed Integer Linear Programming (MILP), which in turn provide exact solutions. The efficiency and sub-optimality of the approach are evaluated on RFs trained on a dataset generated by sampling a bivariate, discontinuous and non-convex benchmark function from the literature. © 2024 IEEE.},
note = {All Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
The problem of global optimization of an objective function represented by a Random Forest (RF) is considered. A method to obtain an approximate solution at low computational complexity is proposed, resorting to the inherent structure of an RF, which is a non-parametric model that partitions the feature space in convex polytopes according to the training data. The approach selects the optimal solution inside the polytopes corresponding to the best data points. It is shown that the proposed approximate method is significantly more efficient, thus applicable at large scale, than extensive global search algorithms, such as gridding and Mixed Integer Linear Programming (MILP), which in turn provide exact solutions. The efficiency and sub-optimality of the approach are evaluated on RFs trained on a dataset generated by sampling a bivariate, discontinuous and non-convex benchmark function from the literature. © 2024 IEEE.
18.
Mohammed, Tareg; Oland, Espen; Fagiano, Lorenzo
Fault Tolerant Flight Control for the Traction Phase of Pumping Airborne Wind Energy Systems Journal Article
In: International Journal of Control, Automation and Systems, vol. 22, no. 8, pp. 2428 – 2443, 2024.
@article{Mohammed20242428,
title = {Fault Tolerant Flight Control for the Traction Phase of Pumping Airborne Wind Energy Systems},
author = {Tareg Mohammed and Espen Oland and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197271366&doi=10.1007%2fs12555-023-0588-z&partnerID=40&md5=b620f461021d1b226be111de27809217},
doi = {10.1007/s12555-023-0588-z},
year = {2024},
date = {2024-01-01},
journal = {International Journal of Control, Automation and Systems},
volume = {22},
number = {8},
pages = {2428 – 2443},
abstract = {A fault-tolerant control approach is proposed, for a pumping airborne wind energy system (AWES) comprising a tethered fixed-wing aircraft with integrated propellers for vertical take-off and landing (VTOL). First, the flight control design for the traction phase of the system, when the tethered aircraft has to fly in loops using the rudder, is presented. Then, the presence of the propellers, that are normally not used in the traction phase, is exploited to obtain a fault tolerant controller in case of rudder malfunctioning. The approach detects a possible discrete control surface fault and compensates for the loss in actuation by using the VTOL system. A sophisticated model of the system is used to analyse the performance of the proposed technique. The main finding is that the approach is able to handle abrupt rudder faults with high tolerance. © ICROS, KIEE and Springer 2024.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A fault-tolerant control approach is proposed, for a pumping airborne wind energy system (AWES) comprising a tethered fixed-wing aircraft with integrated propellers for vertical take-off and landing (VTOL). First, the flight control design for the traction phase of the system, when the tethered aircraft has to fly in loops using the rudder, is presented. Then, the presence of the propellers, that are normally not used in the traction phase, is exploited to obtain a fault tolerant controller in case of rudder malfunctioning. The approach detects a possible discrete control surface fault and compensates for the loss in actuation by using the VTOL system. A sophisticated model of the system is used to analyse the performance of the proposed technique. The main finding is that the approach is able to handle abrupt rudder faults with high tolerance. © ICROS, KIEE and Springer 2024.
19.
Santis, Sonia De; Boffadossi, Roberto; Fagiano, Lorenzo
Automatic Routing Reconfiguration for Fault Tolerance in Smart Manufacturing Plants Conference
vol. 56, no. 2, 2023, (All Open Access, Gold Open Access, Green Open Access).
@conference{DeSantis20235655,
title = {Automatic Routing Reconfiguration for Fault Tolerance in Smart Manufacturing Plants},
author = {Sonia De Santis and Roberto Boffadossi and Lorenzo Fagiano},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184960526&doi=10.1016%2fj.ifacol.2023.10.489&partnerID=40&md5=0093f439e43045577de1b3fbc67906d6},
doi = {10.1016/j.ifacol.2023.10.489},
year = {2023},
date = {2023-01-01},
journal = {IFAC-PapersOnLine},
volume = {56},
number = {2},
pages = {5655 – 5660},
abstract = {This paper focuses on the parts routing problem in a reconfigurable manufacturing plant, in presence of potential faults and uncertainty on the job scheduling and duration. The plant is modeled as a directed graph, where the nodes represent either transportation modules or machines, and the edges represent the allowed transitions between adjacent nodes. The parts move across the plant along predefined sequences of nodes, therefore the system state tracks the progress of the parts along such sequences and the control inputs are the transitions to be activated to command the parts movement. Provided the sequences, the proposed method automatically generates feedback control rules for deadlock avoidance, which are employed by a path following strategy to compute the suitable control inputs, complying with given temporal-logic constraints and avoiding deadlock states. Additionally, the approach is extended to deal with faults affecting the transportation modules via the selection of new feasible sequences and the online reconfiguration of the system state. Finally, the proposed approach is tested in high-fidelity simulations, showing high computational efficiency and throughput. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)},
note = {All Open Access, Gold Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
This paper focuses on the parts routing problem in a reconfigurable manufacturing plant, in presence of potential faults and uncertainty on the job scheduling and duration. The plant is modeled as a directed graph, where the nodes represent either transportation modules or machines, and the edges represent the allowed transitions between adjacent nodes. The parts move across the plant along predefined sequences of nodes, therefore the system state tracks the progress of the parts along such sequences and the control inputs are the transitions to be activated to command the parts movement. Provided the sequences, the proposed method automatically generates feedback control rules for deadlock avoidance, which are employed by a path following strategy to compute the suitable control inputs, complying with given temporal-logic constraints and avoiding deadlock states. Additionally, the approach is extended to deal with faults affecting the transportation modules via the selection of new feasible sequences and the online reconfiguration of the system state. Finally, the proposed approach is tested in high-fidelity simulations, showing high computational efficiency and throughput. Copyright © 2023 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)
20.
Saccani, Danilo; Fagiano, Lorenzo; Zeilinger, Melanie N.; Carron, Andrea
2023, (All Open Access, Green Open Access).
@conference{Saccani20233764,
title = {Model Predictive Control for Multi-Agent Systems Under Limited Communication and Time-Varying Network Topology},
author = {Danilo Saccani and Lorenzo Fagiano and Melanie N. Zeilinger and Andrea Carron},
url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184806455&doi=10.1109%2fCDC49753.2023.10383790&partnerID=40&md5=66c064044a2820178ecdc142a4cab67d},
doi = {10.1109/CDC49753.2023.10383790},
year = {2023},
date = {2023-01-01},
journal = {Proceedings of the IEEE Conference on Decision and Control},
pages = {3764 – 3769},
abstract = {In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from mobile robot swarms, in this paper, we address this problem by optimizing individual agent performance while guaranteeing persistent constraint satisfaction in presence of bounded communication range and time-varying network topology. The approach we propose is a model predictive control (MPC) formulation, building on multi-trajectory MPC (mt-MPC) concepts. To enable plug and play operations when the system is in closed-loop without the need of a request, the proposed MPC scheme predicts two different state trajectories in the same finite horizon optimal control problem. One trajectory drives the system to the desired target, assuming that the network topology will not change in the prediction horizon, while the second one ensures constraint satisfaction assuming a worst-case scenario in terms of new agents joining the network in the planning horizon. Recursive feasibility and stability of the closed-loop system during plug and play operations are shown. The approach effectiveness is illustrated with a numerical simulation. © 2023 IEEE.},
note = {All Open Access, Green Open Access},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from mobile robot swarms, in this paper, we address this problem by optimizing individual agent performance while guaranteeing persistent constraint satisfaction in presence of bounded communication range and time-varying network topology. The approach we propose is a model predictive control (MPC) formulation, building on multi-trajectory MPC (mt-MPC) concepts. To enable plug and play operations when the system is in closed-loop without the need of a request, the proposed MPC scheme predicts two different state trajectories in the same finite horizon optimal control problem. One trajectory drives the system to the desired target, assuming that the network topology will not change in the prediction horizon, while the second one ensures constraint satisfaction assuming a worst-case scenario in terms of new agents joining the network in the planning horizon. Recursive feasibility and stability of the closed-loop system during plug and play operations are shown. The approach effectiveness is illustrated with a numerical simulation. © 2023 IEEE.