Publications
101.
Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo
On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments Journal Article
In: Journal of Process Control, vol. 114, pp. 92-104, 2022, ISSN: 0959-1524.
@article{bonassi2022survey,
title = {On Recurrent Neural Networks for learning-based control: recent results and ideas for future developments},
author = {Fabio Bonassi and Marcello Farina and Jing Xie and Riccardo Scattolini},
url = {https://arxiv.org/abs/2111.13557},
doi = {10.1016/j.jprocont.2022.04.011},
issn = {0959-1524},
year = {2022},
date = {2022-01-01},
journal = {Journal of Process Control},
volume = {114},
pages = {92-104},
abstract = {This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications. The main families of RNN are considered, namely Neural Nonlinear AutoRegressive eXogenous, Echo State Networks, Long Short Term Memory, and Gated Recurrent Units. The goal is twofold. Firstly, to survey recent results concerning the training of RNN that enjoy Input-to-State Stability (ISS) and Incremental Input-to-State Stability (𝛿ISS) guarantees. Secondly, to discuss the issues that still hinder the widespread use of RNN for control, namely their robustness, verifiability, and interpretability. The former properties are related to the so-called generalization capabilities of the networks, i.e. their consistency with the underlying real plants, even in presence of unseen or perturbed input trajectories. The latter is instead related to the possibility of providing a clear formal connection between the RNN model and the plant. In this context, we illustrate how ISS and 𝛿ISS represent a significant step towards the robustness and verifiability of the RNN models, while the requirement of interpretability paves the way to the use of physics-based networks. The design of model predictive controllers with RNN as plant’s model is also briefly discussed. Lastly, some of the main topics of the paper are illustrated on a simulated chemical system.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper aims to discuss and analyze the potentialities of Recurrent Neural Networks (RNN) in control design applications. The main families of RNN are considered, namely Neural Nonlinear AutoRegressive eXogenous, Echo State Networks, Long Short Term Memory, and Gated Recurrent Units. The goal is twofold. Firstly, to survey recent results concerning the training of RNN that enjoy Input-to-State Stability (ISS) and Incremental Input-to-State Stability (𝛿ISS) guarantees. Secondly, to discuss the issues that still hinder the widespread use of RNN for control, namely their robustness, verifiability, and interpretability. The former properties are related to the so-called generalization capabilities of the networks, i.e. their consistency with the underlying real plants, even in presence of unseen or perturbed input trajectories. The latter is instead related to the possibility of providing a clear formal connection between the RNN model and the plant. In this context, we illustrate how ISS and 𝛿ISS represent a significant step towards the robustness and verifiability of the RNN models, while the requirement of interpretability paves the way to the use of physics-based networks. The design of model predictive controllers with RNN as plant’s model is also briefly discussed. Lastly, some of the main topics of the paper are illustrated on a simulated chemical system.
102.
Acerbo, Flavia Sofia; Swevers, Jan; Tuytelaars, Tinne; Son, Tong Duy
MPC-based Imitation Learning for Safe and Human-like Autonomous Driving Workshop
2022.
@workshop{acerbo2022mpc,
title = {MPC-based Imitation Learning for Safe and Human-like Autonomous Driving},
author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},
doi = {https://doi.org/10.48550/arXiv.2206.12348},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2206.12348},
abstract = {To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle to provide safety guarantees on the resulting closed-loop system trajectories. On the other hand, Model Predictive Control (MPC) can handle nonlinear systems with safety constraints, but realizing human-like driving with it requires extensive domain knowledge. This work suggests the use of a seamless combination of the two techniques to learn safe AV controllers from demonstrations of desired driving behaviors, by using MPC as a differentiable control layer within a hierarchical IL policy. With this strategy, IL is performed in closed-loop and end-to-end, through parameters in the MPC cost, model or constraints. Experimental results of this methodology are analyzed for the design of a lane keeping control system, learned via behavioral cloning from observations (BCO), given human demonstrations on a fixed-base driving simulator.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
To ensure user acceptance of autonomous vehicles (AVs), control systems are being developed to mimic human drivers from demonstrations of desired driving behaviors. Imitation learning (IL) algorithms serve this purpose, but struggle to provide safety guarantees on the resulting closed-loop system trajectories. On the other hand, Model Predictive Control (MPC) can handle nonlinear systems with safety constraints, but realizing human-like driving with it requires extensive domain knowledge. This work suggests the use of a seamless combination of the two techniques to learn safe AV controllers from demonstrations of desired driving behaviors, by using MPC as a differentiable control layer within a hierarchical IL policy. With this strategy, IL is performed in closed-loop and end-to-end, through parameters in the MPC cost, model or constraints. Experimental results of this methodology are analyzed for the design of a lane keeping control system, learned via behavioral cloning from observations (BCO), given human demonstrations on a fixed-base driving simulator.
103.
Saccani, Danilo; Cecchin, Leonardo; Fagiano, Lorenzo
Multitrajectory Model Predictive Control for Safe UAV Navigation in an Unknown Environment Journal Article
In: IEEE Transactions on Control Systems Technology, pp. 1-16, 2022.
@article{9938397,
title = {Multitrajectory Model Predictive Control for Safe UAV Navigation in an Unknown Environment},
author = {Danilo Saccani and Leonardo Cecchin and Lorenzo Fagiano},
doi = {10.1109/TCST.2022.3216989},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Control Systems Technology},
pages = {1-16},
abstract = {The problem of navigating an unmanned aerial vehicle (UAV) in an unknown environment is addressed with a novel model predictive control (MPC) formulation, named multitrajectory MPC (mt-MPC). The objective is to safely drive the vehicle to the desired target location by relying only on the partial description of the surroundings provided by an exteroceptive sensor. This information results in time-varying constraints during the navigation among obstacles. The proposed mt-MPC generates a sequence of position set points that are fed to control loops at lower hierarchical levels. To do so, the mt-MPC predicts two different state trajectories, a safe one and an exploiting one, in the same finite horizon optimal control problem (FHOCP). This formulation, particularly suitable for problems with uncertain time-varying constraints, allows one to partially decouple constraint satisfaction (safety) from cost function minimization (exploitation). Uncertainty due to modeling errors and sensors noise is taken into account as well, in a set membership (SM) framework. Theoretical guarantees of persistent obstacle avoidance are derived under suitable assumptions, and the approach is demonstrated experimentally out-of-the-laboratory on a prototype built with off-the-shelf components.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The problem of navigating an unmanned aerial vehicle (UAV) in an unknown environment is addressed with a novel model predictive control (MPC) formulation, named multitrajectory MPC (mt-MPC). The objective is to safely drive the vehicle to the desired target location by relying only on the partial description of the surroundings provided by an exteroceptive sensor. This information results in time-varying constraints during the navigation among obstacles. The proposed mt-MPC generates a sequence of position set points that are fed to control loops at lower hierarchical levels. To do so, the mt-MPC predicts two different state trajectories, a safe one and an exploiting one, in the same finite horizon optimal control problem (FHOCP). This formulation, particularly suitable for problems with uncertain time-varying constraints, allows one to partially decouple constraint satisfaction (safety) from cost function minimization (exploitation). Uncertainty due to modeling errors and sensors noise is taken into account as well, in a set membership (SM) framework. Theoretical guarantees of persistent obstacle avoidance are derived under suitable assumptions, and the approach is demonstrated experimentally out-of-the-laboratory on a prototype built with off-the-shelf components.
104.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Auweraer, Herman Van; Son, Tong Duy
Sim2real for Autonomous Vehicle Control using Executable Digital Twin Proceedings Article
In: 10th IFAC Symposium on Advances in Automotive Control (AAC), pp. 385-391, Elsevier Ltd, 2022, ISSN: 2405-8963.
@inproceedings{ALLAMAA2022385,
title = {Sim2real for Autonomous Vehicle Control using Executable Digital Twin},
author = {Jean Pierre Allamaa and Panagiotis Patrinos and Herman Van Auweraer and Tong Duy Son},
url = {https://www.sciencedirect.com/science/article/pii/S2405896322023461},
doi = {https://doi.org/10.1016/j.ifacol.2022.10.314},
issn = {2405-8963},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {10th IFAC Symposium on Advances in Automotive Control (AAC)},
journal = {IFAC-PapersOnLine},
volume = {55},
number = {24},
pages = {385-391},
publisher = {Elsevier Ltd},
abstract = {In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this work, we propose a sim2real method to transfer and adapt a nonlinear model predictive controller (NMPC) from simulation to the real target system based on executable digital twin (xDT). The xDT model is a high fidelity vehicle dynamics simulator, executable online in the control parameter randomization and learning process. The parameters are adapted to gradually improve control performance and deal with changing real-world environment. In particular, the performance metric is not required to be differentiable nor analytical with respect to the control parameters and system dynamics are not necessary linearized. Eventually, the proposed sim2real framework leverages altogether online high fidelity simulator, data-driven estimations, and simulation based optimization to transfer and adapt efficiently a controller developed in simulation environment to the real platform. Our experiment demonstrates that a high control performance is achieved without tedious time and labor consuming tuning.
105.
Bonassi, Fabio; Xie, Jing; Farina, Marcello; Scattolini, Riccardo
Towards lifelong learning of Recurrent Neural Networks for control design Proceedings Article
In: 2022 European Control Conference (ECC), pp. 2018–2023, IEEE 2022.
@inproceedings{Bonassi2022,
title = {Towards lifelong learning of Recurrent Neural Networks for control design},
author = {Fabio Bonassi and Jing Xie and Marcello Farina and Riccardo Scattolini},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 European Control Conference (ECC)},
pages = {2018--2023},
organization = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
106.
Bodard, Alexander; Moran, Ruairi; Schuurmans, Mathijs; Patrinos, Panagiotis; Sopasakis, Pantelis
SPOCK: A Proximal Method for Multistage Risk-Averse Optimal Control Problems Working paper
2022.
@workingpaper{bodardSPOCKProximalMethod2022,
title = {SPOCK: A Proximal Method for Multistage Risk-Averse Optimal Control Problems},
author = {Alexander Bodard and Ruairi Moran and Mathijs Schuurmans and Panagiotis Patrinos and Pantelis Sopasakis},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2212.01110},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
107.
Bonassi, Fabio; Scattolini, Riccardo
Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems Journal Article
In: European Journal of Control, vol. 65, pp. 100632, 2022, ISSN: 0947-3580.
@article{bonassi2022imc,
title = {Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems},
author = {Fabio Bonassi and Riccardo Scattolini},
url = {https://doi.org/10.1016/j.ejcon.2022.100632
http://arxiv.org/abs/2108.04585},
doi = {10.1016/j.ejcon.2022.100632},
issn = {0947-3580},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {European Journal of Control},
volume = {65},
pages = {100632},
abstract = {Owing to their superior modeling capabilities, gated Recurrent Neural Networks (RNNs), such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, a first gated RNN is used to learn a model of the unknown input-output stable plant. Then, another gated RNN approximating the model inverse is trained. The proposed scheme is able to cope with the saturation of the control variables, and it can be deployed on low-power embedded controllers since it does not require any online computation. The approach is then tested on the Quadruple Tank benchmark system, resulting in satisfactory closed-loop performances.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Owing to their superior modeling capabilities, gated Recurrent Neural Networks (RNNs), such as Gated Recurrent Units (GRUs) and Long Short-Term Memory networks (LSTMs), have become popular tools for learning dynamical systems. This paper aims to discuss how these networks can be adopted for the synthesis of Internal Model Control (IMC) architectures. To this end, a first gated RNN is used to learn a model of the unknown input-output stable plant. Then, another gated RNN approximating the model inverse is trained. The proposed scheme is able to cope with the saturation of the control variables, and it can be deployed on low-power embedded controllers since it does not require any online computation. The approach is then tested on the Quadruple Tank benchmark system, resulting in satisfactory closed-loop performances.
108.
Coppens, Peter; Patrinos, Panagiotis
Policy Iteration Using Q-functions: Linear Dynamics with Multiplicative Noise Working paper
2022.
@workingpaper{coppensPolicyIterationUsing2022,
title = {Policy Iteration Using Q-functions: Linear Dynamics with Multiplicative Noise},
author = {Peter Coppens and Panagiotis Patrinos},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2212.01192},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
109.
Hermans, Ben; Themelis, Andreas; Patrinos, Panagiotis
QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs Journal Article
In: Math. Prog. Comp., vol. 14, no. 3, pp. 497–541, 2022, ISSN: 1867-2957.
@article{hermansQPALMProximalAugmented2022,
title = {QPALM: A Proximal Augmented Lagrangian Method for Nonconvex Quadratic Programs},
author = {Ben Hermans and Andreas Themelis and Panagiotis Patrinos},
doi = {10.1007/s12532-022-00218-0},
issn = {1867-2957},
year = {2022},
date = {2022-01-01},
journal = {Math. Prog. Comp.},
volume = {14},
number = {3},
pages = {497--541},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
110.
Ionescu, Tudor C.; Bourkhissi, Lahcen El; Necoara, Ion
Least Squares Moment Matching-Based Model Reduction Using Convex Optimization Proceedings Article
In: 2022 26th International Conference on System Theory, Control and Computing (ICSTCC), pp. 325–330, 2022, ISSN: 2372-1618.
@inproceedings{ionescuLeastSquaresMoment2022,
title = {Least Squares Moment Matching-Based Model Reduction Using Convex Optimization},
author = {Tudor C. Ionescu and Lahcen El Bourkhissi and Ion Necoara},
doi = {10.1109/ICSTCC55426.2022.9931837},
issn = {2372-1618},
year = {2022},
date = {2022-01-01},
booktitle = {2022 26th International Conference on System Theory, Control and Computing (ICSTCC)},
pages = {325--330},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
111.
Pas, Pieter; Schuurmans, Mathijs; Patrinos, Panagiotis
Alpaqa: A Matrix-Free Solver for Nonlinear MPC and Large-Scale Nonconvex Optimization Proceedings Article
In: 2022 European Control Conference (ECC), pp. 417–422, 2022.
@inproceedings{pasAlpaqaMatrixfreeSolver2022,
title = {Alpaqa: A Matrix-Free Solver for Nonlinear MPC and Large-Scale Nonconvex Optimization},
author = {Pieter Pas and Mathijs Schuurmans and Panagiotis Patrinos},
doi = {10.23919/ECC55457.2022.9838172},
year = {2022},
date = {2022-01-01},
booktitle = {2022 European Control Conference (ECC)},
pages = {417--422},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
112.
Pethick, Thomas; Latafat, Puya; Patrinos, Panagiotis; Fercoq, Olivier; Cevher, Volkan
Escaping Limit Cycles: Global Convergence for Constrained Nonconvex-Nonconcave Minimax Problems Proceedings Article
In: International Conference on Learning Representations, online, France, 2022.
@inproceedings{pethickEscapingLimitCycles2022,
title = {Escaping Limit Cycles: Global Convergence for Constrained Nonconvex-Nonconcave Minimax Problems},
author = {Thomas Pethick and Puya Latafat and Panagiotis Patrinos and Olivier Fercoq and Volkan Cevher},
year = {2022},
date = {2022-01-01},
booktitle = {International Conference on Learning Representations},
address = {online, France},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
113.
Rotaru, Teodor; cois Glineur, Franc; Patrinos, Panagiotis
Tight Convergence Rates of the Gradient Method on Smooth Hypoconvex Functions Working paper
2022.
@workingpaper{rotaruTightConvergenceRates2022,
title = {Tight Convergence Rates of the Gradient Method on Smooth Hypoconvex Functions},
author = {Teodor Rotaru and Franc cois Glineur and Panagiotis Patrinos},
doi = {10.48550/arXiv.2203.00775},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2203.00775},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
114.
Themelis, Andreas; Stella, Lorenzo; Patrinos, Panagiotis
Douglas–Rachford Splitting and ADMM for Nonconvex Optimization: Accelerated and Newton-type Linesearch Algorithms Journal Article
In: Comput Optim Appl, vol. 82, no. 2, pp. 395–440, 2022, ISSN: 1573-2894.
@article{themelisDouglasRachfordSplitting2022,
title = {Douglas–Rachford Splitting and ADMM for Nonconvex Optimization: Accelerated and Newton-type Linesearch Algorithms},
author = {Andreas Themelis and Lorenzo Stella and Panagiotis Patrinos},
doi = {10.1007/s10589-022-00366-y},
issn = {1573-2894},
year = {2022},
date = {2022-01-01},
journal = {Comput Optim Appl},
volume = {82},
number = {2},
pages = {395--440},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
115.
Kessler, Nicolas; Fagiano, Lorenzo
On Control of Phase Transitions in Airborne Wind Energy Systems Workshop
2022.
@workshop{kessler2022control,
title = {On Control of Phase Transitions in Airborne Wind Energy Systems},
author = {Nicolas Kessler and Lorenzo Fagiano},
url = {https://repository.tudelft.nl/file/File_be6082e6-7052-44c7-ae2a-9e2597f291cf},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
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pubstate = {published},
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}