Publications
61.
Cecchin, Leonardo; Baumgärtner, Katrin; Gering, Stefan; Diehl, Moritz
Locally Weighted Regression with Approximate Derivatives for Data-based optimization Proceedings Article
In: 2023 European Control Conference (ECC), pp. 1–6, IEEE 2023.
@inproceedings{cecchin2023locally,
title = {Locally Weighted Regression with Approximate Derivatives for Data-based optimization},
author = { Leonardo Cecchin and Katrin Baumgärtner and Stefan Gering and Moritz Diehl},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 European Control Conference (ECC)},
pages = {1–6},
organization = {IEEE},
abstract = {Interpolation and approximation of data provided in terms of a Look-Up Table (LUT) is a common and well-known task, and is especially relevant for industrial applications. When using the function for point-wise evaluation, the method choice only affects the accuracy of the function value itself. However, when the LUT is used as part of an optimization problem formulation, a bad method choice can prevent convergence or alter significantly the outcome of the solver. Moreover, computational efficiency becomes critical due to the much higher number of evaluations required. This work focuses on a variation of Locally Weighted Regression, with approximate derivatives computation. The result is a method that allows one to obtain the function value together with the first n derivatives, at a reduced computational cost. Theoretical properties of the approach are analyzed, and the results of a minimization problem using the proposed method are compared with more traditional ones. The new approach shows promising performance and results, both for computational efficiency and effectiveness when used in optimization.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interpolation and approximation of data provided in terms of a Look-Up Table (LUT) is a common and well-known task, and is especially relevant for industrial applications. When using the function for point-wise evaluation, the method choice only affects the accuracy of the function value itself. However, when the LUT is used as part of an optimization problem formulation, a bad method choice can prevent convergence or alter significantly the outcome of the solver. Moreover, computational efficiency becomes critical due to the much higher number of evaluations required. This work focuses on a variation of Locally Weighted Regression, with approximate derivatives computation. The result is a method that allows one to obtain the function value together with the first n derivatives, at a reduced computational cost. Theoretical properties of the approach are analyzed, and the results of a minimization problem using the proposed method are compared with more traditional ones. The new approach shows promising performance and results, both for computational efficiency and effectiveness when used in optimization.
62.
Cecchin, Leonardo; Frey, Jonathan; Gering, Stefan; Manderla, Maximilian; Trachte, Adrian; Diehl, Moritz
Nonlinear Model Predictive Control for Efficient Control of Variable Speed Variable Displacement Pumps Proceedings Article
In: 2023 Modeling, Estimation and Control Conference (MECC), pp. 1–6, IFAC 2023.
@inproceedings{cecchin2023nonlinear,
title = {Nonlinear Model Predictive Control for Efficient Control of Variable Speed Variable Displacement Pumps},
author = { Leonardo Cecchin and Jonathan Frey and Stefan Gering and Maximilian Manderla and Adrian Trachte and Moritz Diehl},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 Modeling, Estimation and Control Conference (MECC)},
pages = {1–6},
organization = {IFAC},
abstract = {Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.
63.
Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models Proceedings Article
In: 2022 IEEE 61st Conference on Decision and Control (CDC), pp. 2123-2128, IEEE, 2022, ISBN: 978-1-6654-6761-2.
@inproceedings{bonassi2022offset,
title = {An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models},
author = {Fabio Bonassi and Marcello Farina and Jing Xie and Riccardo Scattolini},
url = {https://doi.org/10.1109/CDC51059.2022.9992362
http://arxiv.org/abs/2203.16290},
doi = {10.1109/CDC51059.2022.9992362},
isbn = {978-1-6654-6761-2},
year = {2022},
date = {2022-12-06},
urldate = {2022-01-01},
booktitle = {2022 IEEE 61st Conference on Decision and Control (CDC)},
journal = {arXiv preprint arXiv:2203.16290},
pages = {2123-2128},
publisher = {IEEE},
abstract = {This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability (δISS) property can be forced when consistent with the behavior of the plant. The δISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper deals with the design of nonlinear MPC controllers that provide offset-free setpoint tracking for models described by Neural Nonlinear AutoRegressive eXogenous (NNARX) networks. The NNARX model is identified from input-output data collected from the plant, and can be given a state-space representation with known measurable states made by past input and output variables, so that a state observer is not required. In the training phase, the Incremental Input-to-State Stability (δISS) property can be forced when consistent with the behavior of the plant. The δISS property is then leveraged to augment the model with an explicit integral action on the output tracking error, which allows to achieve offset-free tracking capabilities to the designed control scheme. The proposed control architecture is numerically tested on a water heating system and the achieved results are compared to those scored by another popular offset-free MPC method, showing that the proposed scheme attains remarkable performances even in presence of disturbances acting on the plant.
64.
Baumgärtner, Katrin; Diehl, Moritz
The Extended Gauss-Newton Method for Nonconvex Loss Functions and its Application to Time-Optimal Model Predictive Control Proceedings Article
In: 2022 American Control Conference (ACC), pp. 4973-4978, IEEE, 2022, ISBN: 978-1-6654-5196-3.
@inproceedings{Baumgaertner2022,
title = {The Extended Gauss-Newton Method for Nonconvex Loss Functions and its Application to Time-Optimal Model Predictive Control},
author = {Katrin Baumgärtner and Moritz Diehl},
url = {https://ieeexplore.ieee.org/document/9867372},
doi = {https://doi.org/10.23919/ACC53348.2022.9867372},
isbn = {978-1-6654-5196-3},
year = {2022},
date = {2022-09-05},
urldate = {2022-09-05},
booktitle = {2022 American Control Conference (ACC)},
pages = {4973-4978},
publisher = {IEEE},
abstract = {We introduce the eXtended Gauss-Newton (XGN) method, which extends the Generalized Gauss-Newton (GGN) method to a class of nonconvex loss functions with a well-behaved global minimum. We show linear local convergence of the XGN method to a stationary point, as well as global convergence in case of a linear residual function and linear constraints. As one possible application, we illustrate how the considered class of nonconvex loss functions can be used to approximate time-optimal behaviour within a Model Predictive Control (MPC) framework.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
We introduce the eXtended Gauss-Newton (XGN) method, which extends the Generalized Gauss-Newton (GGN) method to a class of nonconvex loss functions with a well-behaved global minimum. We show linear local convergence of the XGN method to a stationary point, as well as global convergence in case of a linear residual function and linear constraints. As one possible application, we illustrate how the considered class of nonconvex loss functions can be used to approximate time-optimal behaviour within a Model Predictive Control (MPC) framework.
65.
Wang, Jianhong; Wang, Jinxin; Zhang, Yuan; Gu, Yunjie; Kim, Tae-Kyun
SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning Proceedings Article
In: Advances in Neural Information Processing Systems, 2022, (Accepted at NeurIPS 2022 Conference).
@inproceedings{wang2021shaq,
title = {SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning},
author = {Jianhong Wang and Jinxin Wang and Yuan Zhang and Yunjie Gu and Tae-Kyun Kim},
url = {https://openreview.net/forum?id=BjGawodFnOy
https://arxiv.org/abs/2105.15013},
year = {2022},
date = {2022-07-04},
urldate = {2021-01-01},
booktitle = {Advances in Neural Information Processing Systems},
journal = {arXiv preprint arXiv:2105.15013},
note = {Accepted at NeurIPS 2022 Conference},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
66.
Reiter, Rudolf; Messerer, Florian; Schratter, Markus; Watzenig, Daniel; Diehl, Moritz
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars Proceedings Article
In: 2022 European Control Conference (ECC), pp. 146–153, IEEE, London, United Kingdom, 2022, ISBN: 978-3-907144-07-7.
@inproceedings{reiter_inverse_2022,
title = {An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars},
author = {Rudolf Reiter and Florian Messerer and Markus Schratter and Daniel Watzenig and Moritz Diehl},
url = {https://ieeexplore.ieee.org/document/9838100/},
doi = {10.23919/ECC55457.2022.9838100},
isbn = {978-3-907144-07-7},
year = {2022},
date = {2022-07-01},
urldate = {2022-09-09},
booktitle = {2022 European Control Conference (ECC)},
pages = {146--153},
publisher = {IEEE},
address = {London, United Kingdom},
abstract = {This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes an optimization-based approach to predict trajectories of autonomous race cars. We assume that the observed trajectory is the result of an optimization problem that trades off path progress against acceleration and jerk smoothness, and which is restricted by constraints. The algorithm predicts a trajectory by solving a parameterized nonlinear program (NLP) which contains path progress and smoothness in cost terms. By observing the actual motion of a vehicle, the parameters of prediction are updated by means of solving an inverse optimal control problem that contains the parameters of the predicting NLP as optimization variables. The algorithm therefore learns to predict the observed vehicle trajectory in a least-squares relation to measurement data and to the presumed structure of the predicting NLP. This work contributes with an algorithm that allows for accurate and interpretable predictions with sparse data. The algorithm is implemented on embedded hardware in an autonomous real-world race car that is competing in the challenge Roborace and analyzed with respect to recorded data.
67.
Allamaa, Jean Pierre; Listov, Petr; Auweraer, Herman Van; Jones, Colin; Son, Tong Duy
Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving Proceedings Article
In: 2022 American Control Conference (ACC), pp. 1982-1987, IEEE, Atlanta, GA, USA, 2022, ISBN: 978-1-6654-5196-3.
@inproceedings{Allamaa2022RTNMPCStrategy,
title = {Real-time Nonlinear MPC Strategy with Full Vehicle Validation for Autonomous Driving},
author = {Jean Pierre Allamaa and Petr Listov and Herman Van Auweraer and Colin Jones and Tong Duy Son},
url = {https://ieeexplore.ieee.org/document/9867514},
doi = {10.23919/ACC53348.2022.9867514},
isbn = {978-1-6654-5196-3},
year = {2022},
date = {2022-06-09},
booktitle = {2022 American Control Conference (ACC)},
pages = {1982-1987},
publisher = {IEEE},
address = {Atlanta, GA, USA},
abstract = {In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks. Importantly, the designed algorithms are validated based on a standard automotive XiL development cycle: model-in-the-loop (MiL) with high fidelity vehicle dynamics, hardware-in-the-loop (HiL) with vehicle actuation and embedded platform, and full vehicle-hardware-in-the-loop (VeHiL). The autonomous driving environment contains both virtual simulation and physical proving ground tracks. NMPC algorithms and optimal control problem formulation are fine-tuned using a deployable C code via code generation compatible with the target embedded toolchains. Finally, the developed systems are applied to autonomous collision avoidance, trajectory tracking, and lane change at high speed on city/highway and low speed at a parking environment.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In this paper, we present the development and deployment of an embedded optimal control strategy for autonomous driving applications on a Ford Focus road vehicle. Non-linear model predictive control (NMPC) is designed and deployed on a system with hard real-time constraints. We show the properties of sequential quadratic programming (SQP) optimization solvers that are suitable for driving tasks. Importantly, the designed algorithms are validated based on a standard automotive XiL development cycle: model-in-the-loop (MiL) with high fidelity vehicle dynamics, hardware-in-the-loop (HiL) with vehicle actuation and embedded platform, and full vehicle-hardware-in-the-loop (VeHiL). The autonomous driving environment contains both virtual simulation and physical proving ground tracks. NMPC algorithms and optimal control problem formulation are fine-tuned using a deployable C code via code generation compatible with the target embedded toolchains. Finally, the developed systems are applied to autonomous collision avoidance, trajectory tracking, and lane change at high speed on city/highway and low speed at a parking environment.
68.
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.
69.
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.
70.
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.
71.
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.
72.
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}
}
73.
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}
}
74.
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.
75.
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}
}
76.
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}
}
77.
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}
}
78.
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}
}
79.
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}
}
80.
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}
}