Publications
1.
Messerer, Florian; Baumgärtner, Katrin; Nurkanovic, Armin; Diehl, Moritz
Approximate propagation of normal distributions for stochastic optimal control of nonsmooth systems Journal Article
In: Nonlinear Analysis: Hybrid Systems, vol. 53, pp. 101499, 2024, ISSN: 1751-570X.
@article{MESSERER2024101499,
title = {Approximate propagation of normal distributions for stochastic optimal control of nonsmooth systems},
author = {Florian Messerer and Katrin Baumgärtner and Armin Nurkanovic and Moritz Diehl},
url = {https://www.sciencedirect.com/science/article/pii/S1751570X24000360},
doi = {https://doi.org/10.1016/j.nahs.2024.101499},
issn = {1751-570X},
year = {2024},
date = {2024-08-01},
urldate = {2023-08-14},
journal = {Nonlinear Analysis: Hybrid Systems},
volume = {53},
pages = {101499},
abstract = {We present a method for the approximate propagation of mean and covariance of a probability distribution through ordinary differential equations (ODE) with discontinuous right-hand side. For piecewise affine systems, a normalization of the propagated probability distribution at every time step allows us to analytically compute the expectation integrals of the mean and covariance dynamics while explicitly taking into account the discontinuity. This leads to a natural smoothing of the discontinuity such that for relevant levels of uncertainty the resulting ODE can be integrated directly with standard schemes and it is neither necessary to prespecify the switching sequence nor to use a switch detection method. We then show how this result can be employed in the more general case of piecewise smooth functions based on a structure preserving linearization scheme. The resulting dynamics can be straightforwardly used within standard formulations of stochastic optimal control problems with chance constraints.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We present a method for the approximate propagation of mean and covariance of a probability distribution through ordinary differential equations (ODE) with discontinuous right-hand side. For piecewise affine systems, a normalization of the propagated probability distribution at every time step allows us to analytically compute the expectation integrals of the mean and covariance dynamics while explicitly taking into account the discontinuity. This leads to a natural smoothing of the discontinuity such that for relevant levels of uncertainty the resulting ODE can be integrated directly with standard schemes and it is neither necessary to prespecify the switching sequence nor to use a switch detection method. We then show how this result can be employed in the more general case of piecewise smooth functions based on a structure preserving linearization scheme. The resulting dynamics can be straightforwardly used within standard formulations of stochastic optimal control problems with chance constraints.
2.
Løwenstein, Kristoffer Fink; Bernardini, Daniele; Patrinos, Panagiotis
QPALM-OCP: A Newton-type Proximal Augmented Lagrangian tailored for Quadratic Programs arising in Model Predictive Control Journal Article
In: IEEE Control Systems Letters, 2024, ISSN: 2475-1456, (Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)).
@article{Lowenstein2024QPALMOCP,
title = {QPALM-OCP: A Newton-type Proximal Augmented Lagrangian tailored for Quadratic Programs arising in Model Predictive Control},
author = {Kristoffer Fink Løwenstein and Daniele Bernardini and Panagiotis Patrinos},
doi = {10.1109/LCSYS.2024.3410638},
issn = {2475-1456},
year = {2024},
date = {2024-06-06},
urldate = {2024-04-02},
journal = {IEEE Control Systems Letters},
abstract = {In Model Predictive Control (MPC) fast and reliable Quadratic Programming (QP) solvers are of fundamental importance. The inherent structure of the subsequent Optimal Control Problems (OCPs) can lead to substantial performance improvements if exploited. Therefore, we present a structure-exploiting proximal augmented Lagrangian based solver extending the general-purpose QP-solver QPALM. Our solver relies on semismooth Newton iterates to solve the inner sub-problem while directly accounting for the OCP structure via efficient and sparse matrix factorizations. The matrices to be factorized depends on the active set and therefore low-rank factorization updates can be employed like in active-set methods resulting in cheap iterates. We benchmark our solver against other state-of-the-art QP-solvers and our algorithm compare favorably against these solvers},
note = {Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In Model Predictive Control (MPC) fast and reliable Quadratic Programming (QP) solvers are of fundamental importance. The inherent structure of the subsequent Optimal Control Problems (OCPs) can lead to substantial performance improvements if exploited. Therefore, we present a structure-exploiting proximal augmented Lagrangian based solver extending the general-purpose QP-solver QPALM. Our solver relies on semismooth Newton iterates to solve the inner sub-problem while directly accounting for the OCP structure via efficient and sparse matrix factorizations. The matrices to be factorized depends on the active set and therefore low-rank factorization updates can be employed like in active-set methods resulting in cheap iterates. We benchmark our solver against other state-of-the-art QP-solvers and our algorithm compare favorably against these solvers
3.
Simpson, Léo; Asprion, Jonas; Muntwiler, Simon; Köhler, Johannes; Diehl, Moritz
Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator Working paper
2024, (Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)).
@workingpaper{Simpson2024Parallelizable,
title = {Parallelizable Parametric Nonlinear System Identification via tuning of a Moving Horizon State Estimator},
author = {Léo Simpson and Jonas Asprion and Simon Muntwiler and Johannes Köhler and Moritz Diehl},
doi = {https://doi.org/10.48550/arXiv.2403.17858},
year = {2024},
date = {2024-05-29},
abstract = {This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used for linear system identification, we extend its capabilities to nonlinear systems. The predictions are computed using a moving horizon state estimator with a constant arrival cost. Eventually, both the system parameters and the arrival cost are estimated by minimizing the sum of the squared prediction errors. Since the predictions are induced by the state estimator, the method can be viewed as the tuning of a state estimator, based on its predictive capacities. The present extension of the prediction error method not only enhances performance for nonlinear systems but also enables learning from multiple trajectories with unknown initial states, broadening its applicability in practical scenarios. Additionally, the novel formulation leaves room for the design of efficient and parallelizable optimization algorithms, since each output prediction only depends on a fixed window of past actions and measurements. In the special case of linear time-invariant systems, we show an important property of the proposed method which suggests asymptotic consistency under reasonable assumptions. Numerical examples illustrate the effectiveness and practicality of the approach, and one of the examples also highlights the necessity for the arrival cost.},
note = {Submitted to the 63rd IEEE Conference on Decision and Control 2024 (CDC)},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This paper introduces a novel optimization-based approach for parametric nonlinear system identification. Building upon the prediction error method framework, traditionally used for linear system identification, we extend its capabilities to nonlinear systems. The predictions are computed using a moving horizon state estimator with a constant arrival cost. Eventually, both the system parameters and the arrival cost are estimated by minimizing the sum of the squared prediction errors. Since the predictions are induced by the state estimator, the method can be viewed as the tuning of a state estimator, based on its predictive capacities. The present extension of the prediction error method not only enhances performance for nonlinear systems but also enables learning from multiple trajectories with unknown initial states, broadening its applicability in practical scenarios. Additionally, the novel formulation leaves room for the design of efficient and parallelizable optimization algorithms, since each output prediction only depends on a fixed window of past actions and measurements. In the special case of linear time-invariant systems, we show an important property of the proposed method which suggests asymptotic consistency under reasonable assumptions. Numerical examples illustrate the effectiveness and practicality of the approach, and one of the examples also highlights the necessity for the arrival cost.
4.
Bourkhissi, Lahcen El; Necoara, Ion
Convergence rates for an inexact linearized ADMM for nonsmooth optimization with nonlinear equality constraints Working paper
2024, (Under review).
@workingpaper{bourkhissi2024convergence,
title = {Convergence rates for an inexact linearized ADMM for nonsmooth optimization with nonlinear equality constraints},
author = {Lahcen El Bourkhissi and Ion Necoara},
year = {2024},
date = {2024-05-29},
urldate = {2024-05-29},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
5.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Ohtsuka, Toshiyuki; Son, Tong Duy
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation Proceedings Article Forthcoming
In: Forthcoming, (Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
@inproceedings{allamaa2024RTMPCCBF,
title = {Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation},
author = {Jean Pierre Allamaa and Panagiotis Patrinos and Toshiyuki Ohtsuka and Tong Duy Son},
url = {https://doi.org/10.48550/arXiv.2401.06648},
year = {2024},
date = {2024-05-29},
urldate = {2024-05-29},
abstract = {The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller’s ability to improve crash avoidance by 91%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4.},
note = {Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling such situations. However, standard safety constraints are not scalable and require a long NMPC horizon. Moreover, the adoption of NMPC in the automotive industry is limited by the heavy computation of numerical optimization routines. To address those issues, this paper presents a real-time capable NMPC for automated driving in urban environments, using control barrier functions (CBFs). Furthermore, the designed NMPC is based on a novel collocation transcription approach, named RESAFE/COL, that allows to reduce the number of optimization variables while still guaranteeing the continuous time (nonlinear) inequality constraints satisfaction, through regional convex hull approximation. RESAFE/COL is proven to be 5 times faster than multiple shooting and more tractable for embedded hardware without a decrease in the performance, nor accuracy and safety of the numerical solution. We validate our NMPC-CBF with RESAFE/COL on digital twins of the vehicle and the urban environment and show the safe controller’s ability to improve crash avoidance by 91%. Supplementary visual material can be found at https://youtu.be/_EnbfYwljp4.
6.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Auweraer, Herman Van; Son, Tong Duy
Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin Working paper
2024, (Under review).
@workingpaper{allamaa2024lbMPC,
title = {Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin},
author = {Jean Pierre Allamaa and Panagiotis Patrinos and Herman Van Auweraer and Tong Duy Son},
url = {https://doi.org/10.48550/arXiv.2402.16645},
year = {2024},
date = {2024-05-29},
abstract = {In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for online and on-the-fly adaptation of parametrizable control architectures such that the target closed-loop performance is optimized while accounting for uncertainties as model mismatches, changes in the environment, and task variations. The novelty of the approach resides in leveraging black-box optimization enabled by executable digital twins (xDTs) for data-driven parameter calibration through derivative-free methods to directly adapt the controller in real-time. The xDTs are augmented with Domain Randomization for robustness and allow for safe parameter exploration. The proposed method requires a minimal amount of interaction with the real-world as it pushes the exploration towards the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a nonlinear model predictive controller (NMPC) with 9 parameters, in under 10 minutes. This eliminates the need for hours-long manual tuning and lengthy machine learning training and data collection phases. Our results show that the online adapted NMPC directly compensates for the Sim2Real gap and avoids overtuning in simulation. Importantly, a 75% improvement in tracking performance is achieved and the Sim2Real gap over the target performance is reduced from a factor of 876 to 1.033.},
note = {Under review},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for online and on-the-fly adaptation of parametrizable control architectures such that the target closed-loop performance is optimized while accounting for uncertainties as model mismatches, changes in the environment, and task variations. The novelty of the approach resides in leveraging black-box optimization enabled by executable digital twins (xDTs) for data-driven parameter calibration through derivative-free methods to directly adapt the controller in real-time. The xDTs are augmented with Domain Randomization for robustness and allow for safe parameter exploration. The proposed method requires a minimal amount of interaction with the real-world as it pushes the exploration towards the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a nonlinear model predictive controller (NMPC) with 9 parameters, in under 10 minutes. This eliminates the need for hours-long manual tuning and lengthy machine learning training and data collection phases. Our results show that the online adapted NMPC directly compensates for the Sim2Real gap and avoids overtuning in simulation. Importantly, a 75% improvement in tracking performance is achieved and the Sim2Real gap over the target performance is reduced from a factor of 876 to 1.033.
7.
Zhang, Yuan; Hoffman, Jasper; Boedecker, Joschka
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control Working paper
2024.
@workingpaper{zhang2024uduc,
title = {UDUC: An Uncertainty-driven Approach for Learning-based Robust Control},
author = {Yuan Zhang and Jasper Hoffman and Joschka Boedecker},
url = {arXiv preprint arXiv:2405.02598},
year = {2024},
date = {2024-05-09},
urldate = {2024-05-09},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
8.
Wang, Jianhong; Li, Yang; Zhang, Yuan; Pan, Wei; Kaski, Samuel
Open Ad Hoc Teamwork with Cooperative Game Theory Conference
Forty-first International Conference on Machine Learning, 2024.
@conference{wang2024open,
title = {Open Ad Hoc Teamwork with Cooperative Game Theory},
author = {Jianhong Wang and Yang Li and Yuan Zhang and Wei Pan and Samuel Kaski},
url = {https://openreview.net/forum?id=RlibRvH4B4},
year = {2024},
date = {2024-05-09},
booktitle = {Forty-first International Conference on Machine Learning},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
9.
Zhang, Yuan; Deekshith, Umashankar; Wang, Jianhong; Boedecker, Joschka
LCPPO: An Efficient Multi-agent Reinforcement Learning Algorithm on Complex Railway Network Conference
34th International Conference on Automated Planning and Scheduling, 2024.
@conference{zhanglcppo,
title = {LCPPO: An Efficient Multi-agent Reinforcement Learning Algorithm on Complex Railway Network},
author = {Yuan Zhang and Umashankar Deekshith and Jianhong Wang and Joschka Boedecker},
url = {https://openreview.net/forum?id=gylH3hNASm},
year = {2024},
date = {2024-05-09},
booktitle = {34th International Conference on Automated Planning and Scheduling},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
10.
Shengchao, Yan; Zhang, Yuan; Zhang, Bohe; Boedecker, Joschka; Burgard, Wolfram
Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry Conference
2024 IEEE International Conference on Robotics and Automation ICRA, 2024.
@conference{yan2023geometricb,
title = {Learning Continuous Control with Geometric Regularity from Robot Intrinsic Symmetry},
author = {Yan Shengchao and Yuan Zhang and Bohe Zhang and Joschka Boedecker and Wolfram Burgard},
url = {https://arxiv.org/abs/2306.16316},
year = {2024},
date = {2024-05-09},
booktitle = {2024 IEEE International Conference on Robotics and Automation ICRA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
11.
Zhang, Shuhao; Swevers, Jan
Time-optimal Point-to-point Motion Planning: A Two-stage Approach Proceedings Article Forthcoming
In: Forthcoming, (Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
@inproceedings{Zhang2024TimeOpt,
title = {Time-optimal Point-to-point Motion Planning: A Two-stage Approach},
author = {Shuhao Zhang and Jan Swevers},
url = {https://doi.org/10.48550/arXiv.2403.03573},
year = {2024},
date = {2024-04-16},
abstract = {This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manageable computational complexity and the avoidance of interpolation errors associated with time scaling, especially when aiming to reach a distant goal. Additionally, an asynchronous nonlinear model predictive control (NMPC) update scheme is integrated with this two-stage approach to address delayed and fluctuating computation times, facilitating online replanning. The effectiveness of the proposed two-stage approach and NMPC implementation is demonstrated through numerical examples centered on autonomous navigation with collision avoidance.},
note = {Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
This paper proposes a two-stage approach to formulate the time-optimal point-to-point motion planning problem, involving a first stage with a fixed time grid and a second stage with a variable time grid. The proposed approach brings benefits through its straightforward optimal control problem formulation with a fixed and low number of control steps for manageable computational complexity and the avoidance of interpolation errors associated with time scaling, especially when aiming to reach a distant goal. Additionally, an asynchronous nonlinear model predictive control (NMPC) update scheme is integrated with this two-stage approach to address delayed and fluctuating computation times, facilitating online replanning. The effectiveness of the proposed two-stage approach and NMPC implementation is demonstrated through numerical examples centered on autonomous navigation with collision avoidance.
12.
Løwenstein, Kristoffer Fink; Fagiano, Lorenzo; Bernardini, Daniele; Bemporad, Alberto
Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural Networks in Gray-box Models Proceedings Forthcoming
Forthcoming, (Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
@proceedings{Lowenstein2024PhysicsInformedRNN,
title = {Physics-Informed Online Learning by Moving Horizon Estimation: Learning Recurrent Neural Networks in Gray-box Models},
author = {Kristoffer Fink Løwenstein and Lorenzo Fagiano and Daniele Bernardini and Alberto Bemporad},
year = {2024},
date = {2024-04-02},
abstract = {In Model Predictive Control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet
simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations.},
note = {Accepted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {proceedings}
}
In Model Predictive Control (MPC) closed-loop performance heavily depends on the quality of the underlying prediction model, where such a model must be accurate and yet
simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations.
simple. A key feature in modern MPC applications is the potential for online model adaptation to cope with time-varying changes, part-to-part variations, and complex features of the system dynamics not caught by models derived from first principles. In this paper, we propose to use a physics-informed, or gray-box, model that extends the physics-based model with a data-driven component, namely a Recurrent Neural Network (RNN). Relying on physics-informed models allows for a rather limited size of the RNN, thereby enhancing online applicability compared to pure black-box models. This work presents a method based on Moving Horizon Estimation (MHE) for simultaneous state estimation and learning of the RNN sub-model, a potentially challenging issue due to limited information available in noisy input output data and lack of knowledge of the internal state of the RNN. We provide a case study on a quadruple tank benchmark showing how the method can cope with part-to-part variations.
13.
Meza, Gonzalo; Løwenstein, Kristoffer Fink; Fagiano, Lorenzo
Obstacle avoidance for a robotic manipulator with linear-quadratic Model Predictive Control Proceedings Article Forthcoming
In: 2024 IEEE 20th International Conference on Automation Science and Engineering (CASE 2024), Forthcoming.
@inproceedings{Meza2024ObstacleMPC,
title = {Obstacle avoidance for a robotic manipulator with linear-quadratic Model Predictive Control},
author = {Gonzalo Meza and Kristoffer Fink Løwenstein and Lorenzo Fagiano },
year = {2024},
date = {2024-04-02},
urldate = {2024-04-02},
booktitle = {2024 IEEE 20th International Conference on Automation Science and Engineering (CASE 2024)},
abstract = {The problem of moving a six-degrees-of-freedom manipulator in an environment with unknown obstacles is considered. The manipulator is assumed to be equipped with an exteroceptive sensor that provides a partial sampling of the surroundings. A hierarchical control layout is proposed: in the outer layer, a path planner generates an obstacle free trajectory based on the available local information; in the inner layer, a Model-Predictive Controller formulated in the joint space tracks the trajectory while reactively avoiding unseen obstacles at a higher rate. By constructing a polytopic under-approximation of the free environment end employing a suitable estimate of the Jacobian matrix of the manipulator, the predictive controller features a convex quadratic cost and linear constraints, thus requiring the solution of a quadratic program at each time step. The proposed method is evaluated on the kinematic model of a MyCobot280 robotic arm, showing the potential for real-time feasibility.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
The problem of moving a six-degrees-of-freedom manipulator in an environment with unknown obstacles is considered. The manipulator is assumed to be equipped with an exteroceptive sensor that provides a partial sampling of the surroundings. A hierarchical control layout is proposed: in the outer layer, a path planner generates an obstacle free trajectory based on the available local information; in the inner layer, a Model-Predictive Controller formulated in the joint space tracks the trajectory while reactively avoiding unseen obstacles at a higher rate. By constructing a polytopic under-approximation of the free environment end employing a suitable estimate of the Jacobian matrix of the manipulator, the predictive controller features a convex quadratic cost and linear constraints, thus requiring the solution of a quadratic program at each time step. The proposed method is evaluated on the kinematic model of a MyCobot280 robotic arm, showing the potential for real-time feasibility.
14.
Messerer, Florian; Baumgärtner, Katrin; Lucia, Sergio; Diehl, Moritz
Fourth-order suboptimality of nominal model predictive control in the presence of uncertainty Journal Article
In: IEEE Control Systems Letters, vol. 8, pp. 508-513, 2024.
@article{messerer2024fourthorder,
title = {Fourth-order suboptimality of nominal model predictive control in the presence of uncertainty},
author = {Florian Messerer and Katrin Baumgärtner and Sergio Lucia and Moritz Diehl},
doi = {10.1109/LCSYS.2024.3396611},
year = {2024},
date = {2024-03-08},
urldate = {2024-03-08},
journal = {IEEE Control Systems Letters},
volume = {8},
pages = {508-513},
abstract = {We investigate the suboptimality resulting from the application of nominal model predictive control (MPC) to a nonlinear discrete time stochastic system. The suboptimality is defined with respect to the corresponding stochastic optimal control problem (OCP) that minimizes the expected cost of the closed loop system. In this context, nominal MPC corresponds to a form of certainty-equivalent control (CEC). We prove that, in a smooth and unconstrained setting, the suboptimality growth is of fourth order with respect to the level of uncertainty, a parameter which we can think of as a standard deviation. This implies that the suboptimality does not grow very quickly as the level of uncertainty is increased, providing further insight into the practical success of nominal MPC. Similarly, the difference between the optimal and suboptimal control inputs is of second order. We illustrate the result on a simple numerical example, which we also use to show how the proven relationship may cease to hold in the presence of state constraints.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
We investigate the suboptimality resulting from the application of nominal model predictive control (MPC) to a nonlinear discrete time stochastic system. The suboptimality is defined with respect to the corresponding stochastic optimal control problem (OCP) that minimizes the expected cost of the closed loop system. In this context, nominal MPC corresponds to a form of certainty-equivalent control (CEC). We prove that, in a smooth and unconstrained setting, the suboptimality growth is of fourth order with respect to the level of uncertainty, a parameter which we can think of as a standard deviation. This implies that the suboptimality does not grow very quickly as the level of uncertainty is increased, providing further insight into the practical success of nominal MPC. Similarly, the difference between the optimal and suboptimal control inputs is of second order. We illustrate the result on a simple numerical example, which we also use to show how the proven relationship may cease to hold in the presence of state constraints.
15.
Xie, Jing; Simpson, Léo; Asprion, Jonas; Scattolini, Riccardo
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units Working paper
2024, (Submitted to 2024 IEEE Conference on Control Technology and Applications).
@workingpaper{xie2024learningbased,
title = {A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units},
author = {Jing Xie and Léo Simpson and Jonas Asprion and Riccardo Scattolini
},
url = {https://arxiv.org/abs/2402.05606},
year = {2024},
date = {2024-02-13},
urldate = {2024-02-13},
abstract = {Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real plant, and a state-space representation with known measurable states consisting of past input and output variables is formulated. Second, a tailored model predictive controller is designed based on the trained NNARX network. The proposed control architecture is experimentally tested on the temperature control units manufactured by Tool-Temp AG. The results achieved are compared with those obtained using a PI controller and a linear MPC. The findings illustrate that the proposed scheme achieves satisfactory tracking performance while incurring the lowest energy cost among the compared controllers.},
note = {Submitted to 2024 IEEE Conference on Control Technology and Applications},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Temperature control is a complex task due to its often unknown dynamics and disturbances. This paper explores the use of Neural Nonlinear AutoRegressive eXogenous (NNARX) models for nonlinear system identification and model predictive control of a temperature control unit. First, the NNARX model is identified from input-output data collected from the real plant, and a state-space representation with known measurable states consisting of past input and output variables is formulated. Second, a tailored model predictive controller is designed based on the trained NNARX network. The proposed control architecture is experimentally tested on the temperature control units manufactured by Tool-Temp AG. The results achieved are compared with those obtained using a PI controller and a linear MPC. The findings illustrate that the proposed scheme achieves satisfactory tracking performance while incurring the lowest energy cost among the compared controllers.
16.
Xie, Jing; Bonassi, Fabio; Scattolini, Riccardo
Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models Working paper Forthcoming
Forthcoming, (Accepted by IEEE Transactions on Automation Science and Engineering).
@workingpaper{xie2024internal,
title = {Internal Model Control design for systems learned by Control Affine Neural Nonlinear Autoregressive Exogenous Models},
author = {Jing Xie and Fabio Bonassi and Riccardo Scattolini},
url = {https://arxiv.org/abs/2402.05607},
year = {2024},
date = {2024-02-13},
urldate = {2024-02-13},
abstract = {This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine structure of the system to achieve improved performance. Coherently with recent literature of neural networks for data-driven control, we first analyze the stability properties of CA-NNARX models, devising sufficient conditions for their incremental Input-to-State Stability (incremental ISS) that can be enforced at the model training stage. The model's stability property is then leveraged to design a stable Internal Model Control (IMC) architecture. The proposed control scheme is tested on a simulated Quadruple Tank benchmark system to address the output reference tracking problem. The results achieved show that (i) the modeling accuracy of CA-NNARX is superior to the one of a standard NNARX model for given weight size and training epochs, and (ii) the proposed IMC law provides performance comparable to the ones of a standard Model Predictive Controller (MPC) at a significantly lower computational burden.},
note = {Accepted by IEEE Transactions on Automation Science and Engineering},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
This paper explores the use of Control Affine Neural Nonlinear AutoRegressive eXogenous (CA-NNARX) models for nonlinear system identification and model-based control design. The idea behind this architecture is to match the known control-affine structure of the system to achieve improved performance. Coherently with recent literature of neural networks for data-driven control, we first analyze the stability properties of CA-NNARX models, devising sufficient conditions for their incremental Input-to-State Stability (incremental ISS) that can be enforced at the model training stage. The model's stability property is then leveraged to design a stable Internal Model Control (IMC) architecture. The proposed control scheme is tested on a simulated Quadruple Tank benchmark system to address the output reference tracking problem. The results achieved show that (i) the modeling accuracy of CA-NNARX is superior to the one of a standard NNARX model for given weight size and training epochs, and (ii) the proposed IMC law provides performance comparable to the ones of a standard Model Predictive Controller (MPC) at a significantly lower computational burden.
17.
Cecchin, Leonardo; Trachte, Adrian; Fagiano, Lorenzo; Diehl, Moritz
Real-time prediction of human-generated reference signals: a case study in advanced digging control Working paper
2024, (Submitted to the 2024 European Control Conference (ECC)).
@workingpaper{cecchin2024pred,
title = {Real-time prediction of human-generated reference signals: a case study in advanced digging control},
author = {Leonardo Cecchin and Adrian Trachte and Lorenzo Fagiano and Moritz Diehl},
year = {2024},
date = {2024-02-12},
urldate = {2024-02-12},
abstract = {Techniques like Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback) can improve the control performance by exploiting a prediction of the reference trajectory, which is assumed to be available. This assumption holds true when pre-defined reference trajectories are known a-priori, e.g. constant or piecewise linear, but fails in applications where a human operator chooses the reference at runtime. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks.
We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.},
note = {Submitted to the 2024 European Control Conference (ECC)},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Techniques like Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback) can improve the control performance by exploiting a prediction of the reference trajectory, which is assumed to be available. This assumption holds true when pre-defined reference trajectories are known a-priori, e.g. constant or piecewise linear, but fails in applications where a human operator chooses the reference at runtime. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks.
We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.
We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.
18.
Cecchin, Leonardo; Ohtsuka, Toshiyuki; Trachte, Adrian; Diehl, Moritz
Model Predictive Controller for Hydraulic Cylinders with Independent Metering Control Valves Working paper
2024, (Submitted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).
@workingpaper{cecchin2024imc,
title = {Model Predictive Controller for Hydraulic Cylinders with Independent Metering Control Valves},
author = {Leonardo Cecchin and Toshiyuki Ohtsuka and Adrian Trachte and Moritz Diehl},
year = {2024},
date = {2024-02-12},
urldate = {2024-02-12},
abstract = {Hydraulic cylinders are pivotal components in various industrial, construction, and off-highway applications, where efficient actuation is crucial for reducing energy consumption, minimizing heat generation, and extending components' lifespan. The integration of Independent Metering Control, a valve topology allowing five valves to independently control the flow, represents a significant advancement in enhancing hydraulic systems' performance. However, the lack of a reliable and flexible control solution remains a challenge. In this paper, we present the implementation of nonlinear Model Predictive Control, using a favorable model formulation and a state-of-the-art solver. We show how it can deliver close-to-optimal performance with real-time capabilities, addressing the current gap in achieving efficient control for hydraulic cylinders with Independent Metering Control.},
note = {Submitted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Hydraulic cylinders are pivotal components in various industrial, construction, and off-highway applications, where efficient actuation is crucial for reducing energy consumption, minimizing heat generation, and extending components' lifespan. The integration of Independent Metering Control, a valve topology allowing five valves to independently control the flow, represents a significant advancement in enhancing hydraulic systems' performance. However, the lack of a reliable and flexible control solution remains a challenge. In this paper, we present the implementation of nonlinear Model Predictive Control, using a favorable model formulation and a state-of-the-art solver. We show how it can deliver close-to-optimal performance with real-time capabilities, addressing the current gap in achieving efficient control for hydraulic cylinders with Independent Metering Control.
19.
Zhang, Shuhao; Bos, Mathis; Vandewal, Bastiaan; Decré, Wilm; Gillis, Joris; Swevers, Jan
Robustified Time-optimal Collision-free Motion Planning for Autonomous Mobile Robots under Disturbance Conditions Working paper Forthcoming
Forthcoming, (Accepted to be presented at the 2024 IEEE International Conference on Robotics and Automation (ICRA)).
@workingpaper{lirias4141698,
title = {Robustified Time-optimal Collision-free Motion Planning for Autonomous Mobile Robots under Disturbance Conditions},
author = {Shuhao Zhang and Mathis Bos and Bastiaan Vandewal and Wilm Decré and Joris Gillis and Jan Swevers},
url = {https://kuleuven.limo.libis.be/discovery/fulldisplay?docid=lirias4141698&context=SearchWebhook&vid=32KUL_KUL:Lirias&search_scope=lirias_profile&adaptor=SearchWebhook&tab=LIRIAS&query=any,contains,LIRIAS4141698&offset=0},
year = {2024},
date = {2024-02-07},
urldate = {2024-02-07},
abstract = {This paper presents a robustified time-optimal motion planning approach for navigating an Autonomous Mobile Robot (AMR) from an initial state to a terminal state without colliding with obstacles and while affected by disturbances modeled as random process and measurement noise. The approach iteratively solves the robustified problem by incorporating updated state-dependent safety margins for collision avoidance, the evolution of which is derived separately from the robustified problem. Additionally, a strategy for selecting an alternative terminal state to reach is introduced, which comes into play when the desired terminal state becomes infeasible considering the uncertainties. Both of these contributions are integrated into a robustified motion planning and control pipeline, the efficacy of which is validated through simulation experiments.},
note = {Accepted to be presented at the 2024 IEEE International Conference on Robotics and Automation (ICRA)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
This paper presents a robustified time-optimal motion planning approach for navigating an Autonomous Mobile Robot (AMR) from an initial state to a terminal state without colliding with obstacles and while affected by disturbances modeled as random process and measurement noise. The approach iteratively solves the robustified problem by incorporating updated state-dependent safety margins for collision avoidance, the evolution of which is derived separately from the robustified problem. Additionally, a strategy for selecting an alternative terminal state to reach is introduced, which comes into play when the desired terminal state becomes infeasible considering the uncertainties. Both of these contributions are integrated into a robustified motion planning and control pipeline, the efficacy of which is validated through simulation experiments.
20.
Zhang, Shuhao; Swevers, Jan
Two-stage Time-optimal Motion Planning Presentation
07.02.2024, (Abstract at the 2024 Benelux Meeting ).
@misc{lirias4141067,
title = {Two-stage Time-optimal Motion Planning},
author = {Shuhao Zhang and Jan Swevers},
year = {2024},
date = {2024-02-07},
urldate = {2024-02-07},
note = {Abstract at the 2024 Benelux Meeting },
keywords = {},
pubstate = {published},
tppubtype = {presentation}
}