Publications
1.
Messerer, Florian; Baumgärtner, Katrin; Diehl, Moritz
A Dual-Control Effect Preserving Formulation for Nonlinear Output-Feedback Stochastic Model Predictive Control With Constraints Journal Article
In: IEEE Control Systems Letters, vol. 7, no. 1171--1176, 2023.
@article{Messerer2023,
title = {A Dual-Control Effect Preserving Formulation for Nonlinear Output-Feedback Stochastic Model Predictive Control With Constraints},
author = {Florian Messerer and Katrin Baumgärtner and Moritz Diehl},
url = {https://ieeexplore.ieee.org/document/9993720},
doi = {10.1109/LCSYS.2022.3230552},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Control Systems Letters},
volume = {7},
number = {1171--1176},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
2.
Reiter, Rudolf; Messerer, Florian; Schratter, Markus; Watzenig, Daniel; Diehl, Moritz
An Inverse Optimal Control Approach for Trajectory Prediction of Autonomous Race Cars Inproceedings
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.
3.
Ghezzi, Andrea; Messerer, Florian; Balocco, Jacopo; Manzoni, Vincenzo; Diehl, Moritz
Implicit and Explicit Dual Model Predictive Control with an Application to Steel Recycling Inproceedings Forthcoming
In: Forthcoming.
@inproceedings{ghezzi2022implicit,
title = {Implicit and Explicit Dual Model Predictive Control with an Application to Steel Recycling},
author = {Andrea Ghezzi and Florian Messerer and Jacopo Balocco and Vincenzo Manzoni and Moritz Diehl},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2204.02282},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
4.
Bonassi, Fabio; Farina, Marcello; Xie, Jing; Scattolini, Riccardo
An Offset-Free Nonlinear MPC scheme for systems learned by Neural NARX models Working paper
2022, (Under review).
@workingpaper{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://arxiv.org/abs/2203.16290},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2203.16290},
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 (deltaISS) property can be forced when consistent with the behavior of the plant. The deltaISS 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.},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
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 (deltaISS) property can be forced when consistent with the behavior of the plant. The deltaISS 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.
5.
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.
6.
Bonassi, Fabio; Scattolini, Riccardo
Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems Journal Article
In: European Journal of Control, pp. 100632, 2022, ISSN: 0947-3580.
@article{bonassi2022imc,
title = {Recurrent neural network-based Internal Model Control of unknown nonlinear stable systems},
author = {Fabio Bonassi and Riccardo Scattolini},
doi = {10.1016/j.ejcon.2022.100632},
issn = {0947-3580},
year = {2022},
date = {2022-01-01},
journal = {European Journal of Control},
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.
7.
Acerbo, Flavia Sofia; Swevers, Jan; Tuytelaars, Tinne; Son, Tong Duy
MPC-based Imitation Learning for Safe and Human-like Autonomous Driving Working paper
2022.
@workingpaper{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},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2206.12348},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
8.
Zhang, Yuan; Wang, Jianhong; Boedecker, Joschka
Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization Working paper
2022.
@workingpaper{zhang2022robust,
title = {Robust Reinforcement Learning in Continuous Control Tasks with Uncertainty Set Regularization},
author = {Yuan Zhang and Jianhong Wang and Joschka Boedecker},
url = {https://arxiv.org/abs/2207.02016},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2207.02016},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
9.
Xie, Jing; Bonassi, Fabio; Farina, Marcello; Scattolini, Riccardo
Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models Working paper
2022.
@workingpaper{https://doi.org/10.48550/arxiv.2210.06801,
title = {Robust offset-free nonlinear model predictive control for systems learned by neural nonlinear autoregressive exogenous models},
author = {Jing Xie and Fabio Bonassi and Marcello Farina and Riccardo Scattolini},
url = {https://arxiv.org/abs/2210.06801},
doi = {10.48550/ARXIV.2210.06801},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
10.
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.
11.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Auweraer, Herman Van; Son, Tong Duy
Safety Envelope for Orthogonal Collocation Methods in Embedded Optimal Control Working paper
2022, (Under review).
@workingpaper{Allamaa2022SafetyEF,
title = {Safety Envelope for Orthogonal Collocation Methods in Embedded Optimal Control},
author = {Jean Pierre Allamaa and Panagiotis Patrinos and Herman Van Auweraer and Tong Duy Son},
url = {https://arxiv.org/abs/2211.14853},
doi = {10.48550/ARXIV.2211.14853},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
abstract = {Orthogonal collocation methods are direct simultaneous approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it more favorable for embedded and real-time NMPC applications. However, collocation approaches lack a guarantee about the safety of the resulting continuous trajectory as inequality constraints are only set on a finite number of collocation points. In this paper we propose a method to efficiently create a convex safety envelope containing the full trajectory such that the solution fully satisfies the OCP constraints. We make use of the Bernstein approximations of a polynomial's extrema and span the solution over an orthogonal basis using the Legendre polynomials. The tightness of the safety envelope estimation, high spectral accuracy of the method in solving the underlying differential equations, fast rate of convergence and little conservatism are properties of the presented approach making it a suitable method for safe real-time NMPC deployment. We show that our method has comparable computational performance to the pseudospectral approaches and can approximate the original OCP more accurately and up to 9 times more quickly than standard multiple-shooting methods in autonomous driving applications, without adding complexity to the formulation.},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Orthogonal collocation methods are direct simultaneous approaches for solving optimal control problems (OCP). A high solution accuracy is achieved with few optimization variables, making it more favorable for embedded and real-time NMPC applications. However, collocation approaches lack a guarantee about the safety of the resulting continuous trajectory as inequality constraints are only set on a finite number of collocation points. In this paper we propose a method to efficiently create a convex safety envelope containing the full trajectory such that the solution fully satisfies the OCP constraints. We make use of the Bernstein approximations of a polynomial's extrema and span the solution over an orthogonal basis using the Legendre polynomials. The tightness of the safety envelope estimation, high spectral accuracy of the method in solving the underlying differential equations, fast rate of convergence and little conservatism are properties of the presented approach making it a suitable method for safe real-time NMPC deployment. We show that our method has comparable computational performance to the pseudospectral approaches and can approximate the original OCP more accurately and up to 9 times more quickly than standard multiple-shooting methods in autonomous driving applications, without adding complexity to the formulation.
12.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Auweraer, Herman Van; Son, Tong Duy
Sim2real for Autonomous Vehicle Control using Executable Digital Twin Journal Article
In: IFAC-PapersOnLine, vol. 55, no. 24, pp. 385-391, 2022, ISSN: 2405-8963, (10th IFAC Symposium on Advances in Automotive Control AAC 2022).
@article{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},
journal = {IFAC-PapersOnLine},
volume = {55},
number = {24},
pages = {385-391},
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.},
note = {10th IFAC Symposium on Advances in Automotive Control AAC 2022},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
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.
13.
Voogd, Kevin; Allamaa, Jean Pierre; Alonso-Mora, Javier; Son, Tong Duy
Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin Working paper
2022, (Under review).
@workingpaper{Voogd2022ReinforcementLF,
title = {Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin},
author = {Kevin Voogd and Jean Pierre Allamaa and Javier Alonso-Mora and Tong Duy Son},
url = {https://arxiv.org/abs/2211.14874},
doi = {10.48550/ARXIV.2211.14874},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
abstract = {Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time consuming. Algorithms are often developed first in simulation and then transferred to the real world, leading to a common sim2real challenge that performance decreases when the domain changes. In this paper, we propose a transfer learning process to minimize the gap by exploiting digital twin technology, relying on a systematic and simultaneous combination of virtual and real world data coming from vehicle dynamics and traffic scenarios. The model and testing environment are evolved from model, hardware to vehicle in the loop and proving ground testing stages, similar to standard development cycle in automotive industry. In particular, we also integrate other transfer learning techniques such as domain randomization and adaptation in each stage. The simulation and real data are gradually incorporated to accelerate and make the transfer learning process more robust. The proposed RL methodology is applied to develop a path following steering controller for an autonomous electric vehicle. After learning and deploying the real-time RL control policy on the vehicle, we obtained satisfactory and safe control performance already from the first deployment, demonstrating the advantages of the proposed digital twin based learning process.},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, unsafe, and time consuming. Algorithms are often developed first in simulation and then transferred to the real world, leading to a common sim2real challenge that performance decreases when the domain changes. In this paper, we propose a transfer learning process to minimize the gap by exploiting digital twin technology, relying on a systematic and simultaneous combination of virtual and real world data coming from vehicle dynamics and traffic scenarios. The model and testing environment are evolved from model, hardware to vehicle in the loop and proving ground testing stages, similar to standard development cycle in automotive industry. In particular, we also integrate other transfer learning techniques such as domain randomization and adaptation in each stage. The simulation and real data are gradually incorporated to accelerate and make the transfer learning process more robust. The proposed RL methodology is applied to develop a path following steering controller for an autonomous electric vehicle. After learning and deploying the real-time RL control policy on the vehicle, we obtained satisfactory and safe control performance already from the first deployment, demonstrating the advantages of the proposed digital twin based learning process.
14.
Wang, Renzi; Schuurmans, Mathijs; Patrinos, Panagiotis
Interaction-aware Model Predictive Control for Autonomous Driving Working paper
2022.
@workingpaper{Wang2022,
title = {Interaction-aware Model Predictive Control for Autonomous Driving},
author = {Renzi Wang and Mathijs Schuurmans and Panagiotis Patrinos},
url = {https://arxiv.org/abs/2211.17053},
doi = {10.48550/ARXIV.2211.17053},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
15.
Bonassi, Fabio; Xie, Jing; Farina, Marcello; Scattolini, Riccardo
Towards lifelong learning of Recurrent Neural Networks for control design Inproceedings
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}
}
16.
Simpson, Léo; Ghezzi, Andrea; Asprion, Jonas; Diehl, Moritz
Parameter Estimation of Linear Dynamical Systems with Gaussian Noise Working paper
2022.
@workingpaper{Simpson2022,
title = {Parameter Estimation of Linear Dynamical Systems with Gaussian Noise},
author = {Léo Simpson and Andrea Ghezzi and Jonas Asprion and Moritz Diehl},
url = {https://arxiv.org/abs/2211.12302},
doi = {10.48550/ARXIV.2211.12302},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
17.
Acerbo, Flavia Sofia; Swevers, Jan; Tuytelaars, Tinne; Son, Tong Duy
Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving Working paper
2022.
@workingpaper{acerboEvaluationMPCbasedImitation2022,
title = {Evaluation of MPC-based Imitation Learning for Human-like Autonomous Driving},
author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},
doi = {10.48550/arXiv.2211.12111},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
number = {arXiv:2211.12111},
publisher = {arXiv},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
18.
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}
}
19.
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{bonassiRecurrentNeuralNetworkbased2022,
title = {Recurrent Neural Network-based Internal Model Control Design for Stable Nonlinear Systems},
author = {Fabio Bonassi and Riccardo Scattolini},
doi = {10.1016/j.ejcon.2022.100632},
issn = {0947-3580},
year = {2022},
date = {2022-01-01},
journal = {European Journal of Control},
volume = {65},
pages = {100632},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
20.
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}
}