Publications
1.
Gottardini, Andrea; Cecchin, Leonardo; Demir, Ozan; Fagiano, Lorenzo
Data-Driven Nonlinear Model Predictive Control for Grading Functions for Excavators Working paper Forthcoming
Forthcoming.
@workingpaper{gottardini_data-driven_2025,
title = {Data-Driven Nonlinear Model Predictive Control for Grading Functions for Excavators},
author = {Andrea Gottardini and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
year = {2025},
date = {2025-12-31},
abstract = {Hydraulic excavators are essential construc-
tion machines widely utilized for ground shaping tasks,
such as horizontal leveling and creating sloped surfaces.
These operations require a high degree of precision, which
can be challenging for unskilled workers.
The implementation of automation in hydraulic machin-
ery has the potential to significantly enhance productivity
by improving accuracy and reducing reliance on highly
trained labor. However, the control of hydraulic systems is
complicated by strong nonlinearities and variability among
machines, making the design of effective controllers a sig-
nificant challenge.
In this paper, we propose a data-driven Model Predictive
Control (MPC) system, initially developed for trajectory
tracking and subsequently adapted for a path following
approach. This adaptation is crucial because the trajectory
tracking method relies on open-loop references, using a
predefined speed profile that does not account for the
dynamics of the hydraulic excavator, potentially leading to
difficult-to-follow trajectories.
The prediction model used in the MPC is based on Linear
Local Neuro-Fuzzy Models, trained with the LOcal LInear
MOdel Tree (LOLIMOT) algorithm, while the linear parame-
ters are refined using the Simulation Error Method (SEM).
The proposed control system was rigorously tested on
a JCB Hydradig 110W following a comprehensive data
collection campaign to obtain the necessary data for the
data-driven model of the hydraulic cylinders.
Results, evaluated using metrics such as root mean
square error (RMSE) between the actual and reference
paths, maximum error, and standard deviation (indicating
oscillations during motion), demonstrate that our approach
outperforms previous data-driven feed-forward controllers,
highlighting its efficacy in enhancing hydraulic automation.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {workingpaper}
}
Hydraulic excavators are essential construc-
tion machines widely utilized for ground shaping tasks,
such as horizontal leveling and creating sloped surfaces.
These operations require a high degree of precision, which
can be challenging for unskilled workers.
The implementation of automation in hydraulic machin-
ery has the potential to significantly enhance productivity
by improving accuracy and reducing reliance on highly
trained labor. However, the control of hydraulic systems is
complicated by strong nonlinearities and variability among
machines, making the design of effective controllers a sig-
nificant challenge.
In this paper, we propose a data-driven Model Predictive
Control (MPC) system, initially developed for trajectory
tracking and subsequently adapted for a path following
approach. This adaptation is crucial because the trajectory
tracking method relies on open-loop references, using a
predefined speed profile that does not account for the
dynamics of the hydraulic excavator, potentially leading to
difficult-to-follow trajectories.
The prediction model used in the MPC is based on Linear
Local Neuro-Fuzzy Models, trained with the LOcal LInear
MOdel Tree (LOLIMOT) algorithm, while the linear parame-
ters are refined using the Simulation Error Method (SEM).
The proposed control system was rigorously tested on
a JCB Hydradig 110W following a comprehensive data
collection campaign to obtain the necessary data for the
data-driven model of the hydraulic cylinders.
Results, evaluated using metrics such as root mean
square error (RMSE) between the actual and reference
paths, maximum error, and standard deviation (indicating
oscillations during motion), demonstrate that our approach
outperforms previous data-driven feed-forward controllers,
highlighting its efficacy in enhancing hydraulic automation.
tion machines widely utilized for ground shaping tasks,
such as horizontal leveling and creating sloped surfaces.
These operations require a high degree of precision, which
can be challenging for unskilled workers.
The implementation of automation in hydraulic machin-
ery has the potential to significantly enhance productivity
by improving accuracy and reducing reliance on highly
trained labor. However, the control of hydraulic systems is
complicated by strong nonlinearities and variability among
machines, making the design of effective controllers a sig-
nificant challenge.
In this paper, we propose a data-driven Model Predictive
Control (MPC) system, initially developed for trajectory
tracking and subsequently adapted for a path following
approach. This adaptation is crucial because the trajectory
tracking method relies on open-loop references, using a
predefined speed profile that does not account for the
dynamics of the hydraulic excavator, potentially leading to
difficult-to-follow trajectories.
The prediction model used in the MPC is based on Linear
Local Neuro-Fuzzy Models, trained with the LOcal LInear
MOdel Tree (LOLIMOT) algorithm, while the linear parame-
ters are refined using the Simulation Error Method (SEM).
The proposed control system was rigorously tested on
a JCB Hydradig 110W following a comprehensive data
collection campaign to obtain the necessary data for the
data-driven model of the hydraulic cylinders.
Results, evaluated using metrics such as root mean
square error (RMSE) between the actual and reference
paths, maximum error, and standard deviation (indicating
oscillations during motion), demonstrate that our approach
outperforms previous data-driven feed-forward controllers,
highlighting its efficacy in enhancing hydraulic automation.
2.
Msaad, Salim; Cecchin, Leonardo; Demir, Ozan; Fagiano, Lorenzo
Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks Proceedings Article Forthcoming
In: 2025 American Control Conference (ACC), Forthcoming.
@inproceedings{msaad_data-driven_2025,
title = {Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks},
author = {Salim Msaad and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
year = {2025},
date = {2025-07-01},
urldate = {2025-07-01},
publisher = {2025 American Control Conference (ACC)},
abstract = {A novel solution to control an hydraulic excavator during grading tasks is proposed, featuring a Model Predictive Controller designed using Local Model Networks (LMNs), i.e. linear time-invariant dynamic models averaged by nonlinear static functions. The Local Linear Models Tree (LoLiMoT) algorithm is employed to derive an LMN from experimental data of a real excavator. Then, a nonlinear MPC law is designed and implemented on the excavator’s embedded control system. To further improve the computational efficiency, a time-varying MPC law is designed as well, where the LMN is linearized in real-time using the previously computed optimal trajectory. Experimental results, conducted with the excavator in realworld conditions, show the effectiveness of both approaches in achieving performance comparable to state-of-the-art solutions, while utilizing a more compact dataset and without the need of the hydraulic cylinders’ pressure measurement.},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
A novel solution to control an hydraulic excavator during grading tasks is proposed, featuring a Model Predictive Controller designed using Local Model Networks (LMNs), i.e. linear time-invariant dynamic models averaged by nonlinear static functions. The Local Linear Models Tree (LoLiMoT) algorithm is employed to derive an LMN from experimental data of a real excavator. Then, a nonlinear MPC law is designed and implemented on the excavator’s embedded control system. To further improve the computational efficiency, a time-varying MPC law is designed as well, where the LMN is linearized in real-time using the previously computed optimal trajectory. Experimental results, conducted with the excavator in realworld conditions, show the effectiveness of both approaches in achieving performance comparable to state-of-the-art solutions, while utilizing a more compact dataset and without the need of the hydraulic cylinders’ pressure measurement.
3.
Abbasi-Esfeden, Ramin; Nurkanovic, Armin; Diehl, Moritz; Patrinos, Panagiotis; Swevers, Jan
2025.
@workingpaper{abbasiesfeden2025efficientmixedintegerformulationiterative,
title = {An Efficient Mixed-Integer Formulation and an Iterative Method for Optimal Control of Switched Systems Under Dwell Time Constraints},
author = {Ramin Abbasi-Esfeden and Armin Nurkanovic and Moritz Diehl and Panagiotis Patrinos and Jan Swevers},
url = {https://arxiv.org/abs/2501.05158},
year = {2025},
date = {2025-01-09},
abstract = {This paper presents an efficient Mixed-Integer Nonlinear Programming (MINLP) formulation for systems with discrete control inputs under dwell time constraints. By viewing such systems as a switched system, the problem is decomposed into a Sequence Optimization (SO) and a Switching Time Optimization (STO) -- the former providing the sequence of the switched system, and the latter calculating the optimal switching times. By limiting the feasible set of SO to subsequences of a master sequence, this formulation requires a small number of binary variables, independent of the number of time discretization nodes. This enables the proposed formulation to provide solutions efficiently, even for large numbers of time discretization nodes. To provide even faster solutions, an iterative algorithm is introduced to heuristically solve STO and SO. The proposed approaches are then showcased on four different switched systems and results demonstrate the efficiency of the MINLP formulation and the iterative algorithm.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This paper presents an efficient Mixed-Integer Nonlinear Programming (MINLP) formulation for systems with discrete control inputs under dwell time constraints. By viewing such systems as a switched system, the problem is decomposed into a Sequence Optimization (SO) and a Switching Time Optimization (STO) -- the former providing the sequence of the switched system, and the latter calculating the optimal switching times. By limiting the feasible set of SO to subsequences of a master sequence, this formulation requires a small number of binary variables, independent of the number of time discretization nodes. This enables the proposed formulation to provide solutions efficiently, even for large numbers of time discretization nodes. To provide even faster solutions, an iterative algorithm is introduced to heuristically solve STO and SO. The proposed approaches are then showcased on four different switched systems and results demonstrate the efficiency of the MINLP formulation and the iterative algorithm.
4.
Abbasi-Esfeden, Ramin; Plate, Christoph; Sager, Sebastian; Swevers, Jan
Dynamic Programming for Mixed Integer Optimal Control Problems with Dwell Time Constraints Working paper
2024.
@workingpaper{AbbasiEsfeden2024,
title = {Dynamic Programming for Mixed Integer Optimal Control Problems with Dwell Time Constraints},
author = {Ramin Abbasi-Esfeden and Christoph Plate and Sebastian Sager and Jan Swevers},
url = {http://dx.doi.org/10.2139/ssrn.5043263},
doi = {10.2139/ssrn.5043263},
year = {2024},
date = {2024-12-17},
journal = {Elsevier BV},
abstract = {This paper introduces Dynamic Programming (DP) as a method for solving the Combinatorial Integral Approximation (CIA) problem within the CIA decomposition approach for Mixed-Integer Optimal Control Problems (MIOCPs). Additionally, we incorporate general dwell time constraints into the DP framework. The proposed method is tested on three MIOCPs with a minimum dwell time constraint, and its performance is compared to the usage of the state-of-the-art general purpose solver GuRoBi (MILP) and to the tailored branch-and-bound (BnB) solver from the pycombina package. The results show that DP is more computationally efficient, and its flexible cost-to-go function formulation makes it suitable for handling cases where simple approximations of the relaxed solution are insufficient.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This paper introduces Dynamic Programming (DP) as a method for solving the Combinatorial Integral Approximation (CIA) problem within the CIA decomposition approach for Mixed-Integer Optimal Control Problems (MIOCPs). Additionally, we incorporate general dwell time constraints into the DP framework. The proposed method is tested on three MIOCPs with a minimum dwell time constraint, and its performance is compared to the usage of the state-of-the-art general purpose solver GuRoBi (MILP) and to the tailored branch-and-bound (BnB) solver from the pycombina package. The results show that DP is more computationally efficient, and its flexible cost-to-go function formulation makes it suitable for handling cases where simple approximations of the relaxed solution are insufficient.
5.
Lahr, Amon; Näf, Joshua; Wabersich, Kim P.; Frey, Jonathan; Siehl, Pascal; Carron, Andrea; Diehl, Moritz; Zeilinger, Melanie N.
L4acados: Learning-based Models for Acados, Applied to Gaussian Process-Based Predictive Control Working paper
2024.
@workingpaper{lahr_l4acados_2024,
title = {L4acados: Learning-based Models for Acados, Applied to Gaussian Process-Based Predictive Control},
author = {Amon Lahr and Joshua Näf and Kim P. Wabersich and Jonathan Frey and Pascal Siehl and Andrea Carron and Moritz Diehl and Melanie N. Zeilinger},
doi = {10.48550/arXiv.2411.19258},
year = {2024},
date = {2024-11-28},
urldate = {2024-11-28},
abstract = {Incorporating learning-based models, such as Gaussian processes (GPs), into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, despite recent advances in numerical optimization and real-time GP inference, its widespread application is limited by the lack of an efficient and modular open-source implementation. This work aims at filling this gap by providing an efficient implementation of zero-order Gaussian process-based MPC in acados, as well as L4acados, a general framework for incorporating non-CasADi (learning-based) residual models in acados. By providing the required sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting custom Jacobian approximations, as well as parallelization of sensitivity computations when preparing the quadratic subproblems. The computational efficiency of L4acados is benchmarked against available software using a neural network-based control example. Last, it is used demonstrate the performance of the zero-order GP-MPC method applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
Incorporating learning-based models, such as Gaussian processes (GPs), into model predictive control (MPC) strategies can significantly improve control performance and online adaptation capabilities for real-world applications. Still, despite recent advances in numerical optimization and real-time GP inference, its widespread application is limited by the lack of an efficient and modular open-source implementation. This work aims at filling this gap by providing an efficient implementation of zero-order Gaussian process-based MPC in acados, as well as L4acados, a general framework for incorporating non-CasADi (learning-based) residual models in acados. By providing the required sensitivities via a user-defined Python module, L4acados enables the implementation of MPC controllers with learning-based residual models in acados, while supporting custom Jacobian approximations, as well as parallelization of sensitivity computations when preparing the quadratic subproblems. The computational efficiency of L4acados is benchmarked against available software using a neural network-based control example. Last, it is used demonstrate the performance of the zero-order GP-MPC method applied to two hardware examples: autonomous miniature racing, as well as motion control of a full-scale autonomous vehicle for an ISO lane change maneuver.
6.
Dong, Shiying; Ghezzi, Andrea; Harzer, Jakob; Frey, Jonathan; Gao, Bingzhao; Chen, Hong; Diehl, Moritz
Real-Time NMPC With Convex--Concave Constraints and Application to Eco-Driving Journal Article
In: IEEE Transactions on Control Systems Technology, 2024.
@article{dong2024real,
title = {Real-Time NMPC With Convex--Concave Constraints and Application to Eco-Driving},
author = {Shiying Dong and Andrea Ghezzi and Jakob Harzer and Jonathan Frey and Bingzhao Gao and Hong Chen and Moritz Diehl},
doi = {10.1109/TCST.2024.3494993},
year = {2024},
date = {2024-11-15},
journal = {IEEE Transactions on Control Systems Technology},
abstract = {In this brief, we propose a novel real-time numerical algorithm for solving nonlinear model predictive control (NMPC) with convex–concave constraints, which arise in various practical applications. Instead of requiring full convergence for each problem at every sampling time, the proposed algorithm, called real-time iteration sequential convex programming (RTI-SCP), solves only one convex subproblem but iterates as the problem evolves. Compared with previous methods, the RTI-SCP adopts a more refined approach by linearizing only the concave components of the constraints. It retains and efficiently utilizes all the underlying convex structures, thereby transforming subproblems into structured forms that can be solved using the existing tools. In addition, to the best of our knowledge, the widely investigated eco-driving control strategy for autonomous vehicles is now formulated for the first time into a convex–concave programming problem with strong theoretical properties. Eventually, the experimental results demonstrate that the proposed strategy can improve computational efficiency and overall control performance, and it is suitable for real-time implementation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this brief, we propose a novel real-time numerical algorithm for solving nonlinear model predictive control (NMPC) with convex–concave constraints, which arise in various practical applications. Instead of requiring full convergence for each problem at every sampling time, the proposed algorithm, called real-time iteration sequential convex programming (RTI-SCP), solves only one convex subproblem but iterates as the problem evolves. Compared with previous methods, the RTI-SCP adopts a more refined approach by linearizing only the concave components of the constraints. It retains and efficiently utilizes all the underlying convex structures, thereby transforming subproblems into structured forms that can be solved using the existing tools. In addition, to the best of our knowledge, the widely investigated eco-driving control strategy for autonomous vehicles is now formulated for the first time into a convex–concave programming problem with strong theoretical properties. Eventually, the experimental results demonstrate that the proposed strategy can improve computational efficiency and overall control performance, and it is suitable for real-time implementation.
7.
Wang, Renzi; Acerbo, Flavia Sofia; Son, Tong Duy; Patrinos, Panagiotis
Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach Working paper
2024.
@workingpaper{wang2024imitation,
title = {Imitation Learning from Observations: An Autoregressive Mixture of Experts Approach},
author = {Renzi Wang and Flavia Sofia Acerbo and Tong Duy Son and Panagiotis Patrinos},
url = {https://doi.org/10.48550/arXiv.2411.08232
},
year = {2024},
date = {2024-11-12},
abstract = {This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging the existing dynamics knowledge, the first stage of the framework estimates the control input sequences and hence reduces the problem complexity. At the second stage, the policy is learned by solving a regularized maximum-likelihood estimation problem using the estimated control input sequences. We further extend the learning procedure by incorporating a Lyapunov stability constraint to ensure asymptotic stability of the identified model, for accurate multi-step predictions. The effectiveness of the proposed framework is validated using two autonomous driving datasets collected from human demonstrations, demonstrating its practical applicability in modelling complex nonlinear dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {workingpaper}
}
This paper presents a novel approach to imitation learning from observations, where an autoregressive mixture of experts model is deployed to fit the underlying policy. The parameters of the model are learned via a two-stage framework. By leveraging the existing dynamics knowledge, the first stage of the framework estimates the control input sequences and hence reduces the problem complexity. At the second stage, the policy is learned by solving a regularized maximum-likelihood estimation problem using the estimated control input sequences. We further extend the learning procedure by incorporating a Lyapunov stability constraint to ensure asymptotic stability of the identified model, for accurate multi-step predictions. The effectiveness of the proposed framework is validated using two autonomous driving datasets collected from human demonstrations, demonstrating its practical applicability in modelling complex nonlinear dynamics.
8.
Zhang, Yuan; Hoffman, Jasper; Boedecker, Joschka
UDUC: An Uncertainty-driven Approach for Learning-based Robust Control Proceedings Article
In: ECAI 2024 - 27th European Conference on Artificial Intelligence - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024), pp. 4402-4409, IOS Press, Santiago de Compostela, Spain, 2024.
@inproceedings{zhang2024uduc,
title = {UDUC: An Uncertainty-driven Approach for Learning-based Robust Control},
author = {Yuan Zhang and Jasper Hoffman and Joschka Boedecker},
url = {https://arxiv.org/abs/2405.02598},
doi = {10.3233/FAIA241018},
year = {2024},
date = {2024-10-24},
urldate = {2024-10-24},
booktitle = {ECAI 2024 - 27th European Conference on Artificial Intelligence - Including 13th Conference on Prestigious Applications of Intelligent Systems (PAIS 2024)},
volume = {392},
pages = {4402-4409},
publisher = {IOS Press},
address = {Santiago de Compostela, Spain},
series = {Frontiers in Artificial Intelligence and Applications},
abstract = {Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the uncertainty-driven robust control (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of the UDUC loss through the lens of robust optimization and evaluate its performance on the challenging real-world reinforcement learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Learning-based techniques have become popular in both model predictive control (MPC) and reinforcement learning (RL). Probabilistic ensemble (PE) models offer a promising approach for modelling system dynamics, showcasing the ability to capture uncertainty and scalability in high-dimensional control scenarios. However, PE models are susceptible to mode collapse, resulting in non-robust control when faced with environments slightly different from the training set. In this paper, we introduce the uncertainty-driven robust control (UDUC) loss as an alternative objective for training PE models, drawing inspiration from contrastive learning. We analyze the robustness of the UDUC loss through the lens of robust optimization and evaluate its performance on the challenging real-world reinforcement learning (RWRL) benchmark, which involves significant environmental mismatches between the training and testing environments.
9.
Acerbo, Flavia Sofia; Swevers, Jan; Tuytelaars, Tinne; Son, Tong Duy
Driving from Vision through Differentiable Optimal Control Proceedings Article
In: 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 2153-0866, IEEE, Abu Dhabi, United Arab Emirates, 2024, ISBN: 979-8-3503-7770-5.
@inproceedings{acerbo24drividoc,
title = {Driving from Vision through Differentiable Optimal Control},
author = {Flavia Sofia Acerbo and Jan Swevers and Tinne Tuytelaars and Tong Duy Son},
url = {https://doi.org/10.48550/arXiv.2403.15102
},
doi = {10.1109/IROS58592.2024.10802306},
isbn = {979-8-3503-7770-5},
year = {2024},
date = {2024-10-01},
booktitle = {2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
pages = {2153-0866},
publisher = {IEEE},
address = {Abu Dhabi, United Arab Emirates},
abstract = {This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations. DriViDOC combines the automatic inference of relevant features from camera frames with the properties of nonlinear model predictive control (NMPC), such as constraint satisfaction. Our approach leverages the differentiability of parametric NMPC, allowing for end-to-end learning of the driving model from images to control. The model is trained on an offline dataset comprising various human demonstrations collected on a motion-base driving simulator. During online testing, the model demonstrates successful imitation of different driving styles, and the interpreted NMPC parameters provide insights into the achievement of specific driving behaviors. Our experimental results show that DriViDOC outperforms other methods involving NMPC and neural networks, exhibiting an average improvement of 20% in imitation scores.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper proposes DriViDOC: a framework for Driving from Vision through Differentiable Optimal Control, and its application to learn autonomous driving controllers from human demonstrations. DriViDOC combines the automatic inference of relevant features from camera frames with the properties of nonlinear model predictive control (NMPC), such as constraint satisfaction. Our approach leverages the differentiability of parametric NMPC, allowing for end-to-end learning of the driving model from images to control. The model is trained on an offline dataset comprising various human demonstrations collected on a motion-base driving simulator. During online testing, the model demonstrates successful imitation of different driving styles, and the interpreted NMPC parameters provide insights into the achievement of specific driving behaviors. Our experimental results show that DriViDOC outperforms other methods involving NMPC and neural networks, exhibiting an average improvement of 20% in imitation scores.
10.
Cupo, Alessandro; Cecchin, Leonardo; Demir, Ozan; Fagiano, Lorenzo
Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators Proceedings Article
In: 4th Modeling, Estimation and Control Conference (MECC), 2024.
@inproceedings{cupo_energy-optimal_2024,
title = {Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators},
author = {Alessandro Cupo and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
year = {2024},
date = {2024-10-01},
publisher = {4th Modeling, Estimation and Control Conference (MECC)},
abstract = {An optimal trajectory planning approach for hydraulic excavator arms is presented, where the goal is to create trajectories that trade-off energy consumption and completion time. We develop a physics-based model of the excavator, which describes both the dynamics and the hydraulic system’s behavior. Further investigation of the Optimal Control Problem, used to create the trajectory, allows for discussion regarding the trade-off between power and time recovering a wide range of solutions based on the designer’s choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
An optimal trajectory planning approach for hydraulic excavator arms is presented, where the goal is to create trajectories that trade-off energy consumption and completion time. We develop a physics-based model of the excavator, which describes both the dynamics and the hydraulic system’s behavior. Further investigation of the Optimal Control Problem, used to create the trajectory, allows for discussion regarding the trade-off between power and time recovering a wide range of solutions based on the designer’s choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path.
11.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Ohtsuka, Toshiyuki; Son, Tong Duy
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation Best Paper Proceedings Article
In: 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, pp. 392-399, IFAC-PapersOnLine, 2024, (Preprint: https://doi.org/10.48550/arXiv.2401.06648).
@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://www.sciencedirect.com/science/article/pii/S240589632401437X},
doi = {https://doi.org/10.1016/j.ifacol.2024.09.058},
year = {2024},
date = {2024-09-25},
urldate = {2024-05-29},
booktitle = {8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024},
volume = {58},
number = {18},
pages = {392-399},
publisher = {IFAC-PapersOnLine},
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 = {Preprint: https://doi.org/10.48550/arXiv.2401.06648},
keywords = {},
pubstate = {published},
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.
12.
Gao, Yunfan; Messerer, Florian; van Duijkeren, Niels; Houska, Boris; Diehl, Moritz
Real-Time-Feasible Collision-Free Motion Planning For Ellipsoidal Objects Proceedings Article Forthcoming
In: Forthcoming, (Accepted at the 2024 Conference on Decision and Control (CDC)).
@inproceedings{24_gao_realtimefeasible,
title = {Real-Time-Feasible Collision-Free Motion Planning For Ellipsoidal Objects},
author = {Yunfan Gao and Florian Messerer and Niels van Duijkeren and Boris Houska and Moritz Diehl},
doi = {https://doi.org/10.48550/arXiv.2409.12007},
year = {2024},
date = {2024-09-18},
abstract = {Online planning of collision-free trajectories is a fundamental task for robotics and self-driving car applications. This paper revisits collision avoidance between ellipsoidal objects using differentiable constraints. Two ellipsoids do not overlap if and only if the endpoint of the vector between the center points of the ellipsoids does not lie in the interior of the Minkowski sum of the ellipsoids. This condition is formulated using a parametric over-approximation of the Minkowski sum, which can be made tight in any given direction. The resulting collision avoidance constraint is included in an optimal control problem (OCP) and evaluated in comparison to the separating-hyperplane approach. Not only do we observe that the Minkowski-sum formulation is computationally more efficient in our experiments, but also that using pre-determined over-approximation parameters based on warm-start trajectories leads to a very limited increase in suboptimality. This gives rise to a novel real-time scheme for collision-free motion planning with model predictive control (MPC). Both the real-time feasibility and the effectiveness of the constraint formulation are demonstrated in challenging real-world experiments.},
note = {Accepted at the 2024 Conference on Decision and Control (CDC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Online planning of collision-free trajectories is a fundamental task for robotics and self-driving car applications. This paper revisits collision avoidance between ellipsoidal objects using differentiable constraints. Two ellipsoids do not overlap if and only if the endpoint of the vector between the center points of the ellipsoids does not lie in the interior of the Minkowski sum of the ellipsoids. This condition is formulated using a parametric over-approximation of the Minkowski sum, which can be made tight in any given direction. The resulting collision avoidance constraint is included in an optimal control problem (OCP) and evaluated in comparison to the separating-hyperplane approach. Not only do we observe that the Minkowski-sum formulation is computationally more efficient in our experiments, but also that using pre-determined over-approximation parameters based on warm-start trajectories leads to a very limited increase in suboptimality. This gives rise to a novel real-time scheme for collision-free motion planning with model predictive control (MPC). Both the real-time feasibility and the effectiveness of the constraint formulation are demonstrated in challenging real-world experiments.
13.
Prajapat, Manish; Lahr, Amon; Köhler, Johannes; Krause, Andreas; Zeilinger, Melanie N.
Towards Safe and Tractable Gaussian Process-Based MPC: Efficient Sampling within a Sequential Quadratic Programming Framework Proceedings Article Forthcoming
In: Forthcoming, (Accepted at the 2024 Conference on Decision and Control (CDC)).
@inproceedings{prajapat_towards_2024,
title = {Towards Safe and Tractable Gaussian Process-Based MPC: Efficient Sampling within a Sequential Quadratic Programming Framework},
author = {Manish Prajapat and Amon Lahr and Johannes Köhler and Andreas Krause and Melanie N. Zeilinger},
doi = {10.48550/arXiv.2409.08616},
year = {2024},
date = {2024-09-13},
urldate = {2024-09-13},
abstract = {Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples.},
note = {Accepted at the 2024 Conference on Decision and Control (CDC)},
keywords = {},
pubstate = {forthcoming},
tppubtype = {inproceedings}
}
Learning uncertain dynamics models using Gaussian process~(GP) regression has been demonstrated to enable high-performance and safety-aware control strategies for challenging real-world applications. Yet, for computational tractability, most approaches for Gaussian process-based model predictive control (GP-MPC) are based on approximations of the reachable set that are either overly conservative or impede the controller's safety guarantees. To address these challenges, we propose a robust GP-MPC formulation that guarantees constraint satisfaction with high probability. For its tractable implementation, we propose a sampling-based GP-MPC approach that iteratively generates consistent dynamics samples from the GP within a sequential quadratic programming framework. We highlight the improved reachable set approximation compared to existing methods, as well as real-time feasible computation times, using two numerical examples.
14.
Simpson, Léo; Xie, Jing; Asprion, Jonas; Scattolini, Riccardo
A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units Proceedings Article
In: 2024 IEEE Conference on Control Technology and Applications (CCTA), pp. 675-680, IEEE, Newcastle upon Tyne, United Kingdom, 2024, ISSN: 2768-0770.
@inproceedings{xie2024learningbased,
title = {A Learning-based Model Predictive Control Scheme with Application to Temperature Control Units},
author = {Léo Simpson and Jing Xie and Jonas Asprion and Riccardo Scattolini
},
url = {https://arxiv.org/abs/2402.05606},
doi = {10.1109/CCTA60707.2024.10666571},
issn = {2768-0770},
year = {2024},
date = {2024-09-11},
urldate = {2024-09-11},
booktitle = {2024 IEEE Conference on Control Technology and Applications (CCTA)},
pages = {675-680},
publisher = {IEEE},
address = {Newcastle upon Tyne, United Kingdom},
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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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.
15.
Gao, Yunfan; Messerer, Florian; van Duijkeren, Niels; Diehl, Moritz
Stochastic Model Predictive Control with Optimal Linear Feedback for Mobile Robots in Dynamic Environments Proceedings Article
In: 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, pp. 153-158, IFAC-PapersOnLine, 2024.
@inproceedings{24_gao_stochasticmpc,
title = {Stochastic Model Predictive Control with Optimal Linear Feedback for Mobile Robots in Dynamic Environments},
author = {Yunfan Gao and Florian Messerer and Niels van Duijkeren and Moritz Diehl},
doi = {https://doi.org/10.1016/j.ifacol.2024.09.024},
year = {2024},
date = {2024-09-03},
urldate = {2024-09-03},
booktitle = {8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024},
volume = {58},
number = {18},
pages = {153-158},
publisher = {IFAC-PapersOnLine},
abstract = {Robot navigation around humans can be a challenging problem since human movements are hard to predict. Stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Robot navigation around humans can be a challenging problem since human movements are hard to predict. Stochastic model predictive control (MPC) can account for such uncertainties and approximately bound the probability of a collision to take place. In this paper, to counteract the rapidly growing human motion uncertainty over time, we incorporate state feedback in the stochastic MPC. This allows the robot to more closely track reference trajectories. To this end the feedback policy is left as a degree of freedom in the optimal control problem. The stochastic MPC with feedback is validated in simulation experiments and is compared against nominal MPC and stochastic MPC without feedback. The added computation time can be limited by reducing the number of additional variables for the feedback law with a small compromise in control performance.
16.
Leeman, Antoine P.; Köhler, Johannes; Messerer, Florian; Lahr, Amon; Diehl, Moritz; Zeilinger, Melanie N.
Fast System Level Synthesis: Robust Model Predictive Control Using Riccati Recursions Proceedings Article
In: 8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024, IFAC-PapersOnLine, 2024.
@inproceedings{leeman_fast_2024,
title = {Fast System Level Synthesis: Robust Model Predictive Control Using Riccati Recursions},
author = {Antoine P. Leeman and Johannes Köhler and Florian Messerer and Amon Lahr and Moritz Diehl and Melanie N. Zeilinger},
url = {https://doi.org/10.48550/arXiv.2401.13762},
doi = {10.1016/j.ifacol.2024.09.027},
year = {2024},
date = {2024-09-01},
urldate = {2024-02-07},
booktitle = {8th IFAC Conference on Nonlinear Model Predictive Control NMPC 2024},
volume = {58},
number = {18},
publisher = {IFAC-PapersOnLine},
abstract = {System Level Synthesis (SLS) enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem. The proposed algorithm builds on a recently proposed joint optimization scheme and iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of O(N2(n3x+n3u)) for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups of order 10 to 10^3 compared to general-purpose commercial solvers.
},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
System Level Synthesis (SLS) enables improved robust MPC formulations by allowing for joint optimization of the nominal trajectory and controller. This paper introduces a tailored algorithm for solving the corresponding disturbance feedback optimization problem. The proposed algorithm builds on a recently proposed joint optimization scheme and iterates between optimizing the controller and the nominal trajectory while converging q-linearly to an optimal solution. We show that the controller optimization can be solved through Riccati recursions leading to a horizon-length, state, and input scalability of O(N2(n3x+n3u)) for each iterate. On a numerical example, the proposed algorithm exhibits computational speedups of order 10 to 10^3 compared to general-purpose commercial solvers.
17.
Allamaa, Jean Pierre; Patrinos, Panagiotis; Auweraer, Herman Van; Son, Tong Duy
Learning-Based NMPC Adaptation for Autonomous Driving Using Parallelized Digital Twin Journal Article
In: IEEE Transactions on Control Systems Technology, vol. Early Access, pp. 1-16, 2024, ISSN: 1063-6536, (Preprint: https://doi.org/10.48550/arXiv.2402.16645).
@article{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},
doi = {10.1109/TCST.2024.3437163},
issn = {1063-6536},
year = {2024},
date = {2024-08-14},
urldate = {2024-05-29},
journal = {IEEE Transactions on Control Systems Technology},
volume = {Early Access},
pages = {1-16},
abstract = {In this work, we focus on the challenge of transferring an autonomous driving (AD) controller from simulation to reality (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 such 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 (RT). The xDTs are augmented with domain randomization (DR) 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 toward the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a nonlinear model predictive control (NMPC) with nine parameters, in under 10 min. 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 = {Preprint: https://doi.org/10.48550/arXiv.2402.16645},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
In this work, we focus on the challenge of transferring an autonomous driving (AD) controller from simulation to reality (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 such 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 (RT). The xDTs are augmented with domain randomization (DR) 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 toward the xDTs. We validate our approach through real-world experiments, demonstrating its effectiveness in transferring and fine-tuning a nonlinear model predictive control (NMPC) with nine parameters, in under 10 min. 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.
18.
Schulz, Felix; Hoffman, Jasper; Zhang, Yuan; Boedecker, Joschka
Learning When to Trust the Expert for Guided Exploration in RL Workshop
2024, (ICML 2024 Workshop: Foundations of Reinforcement Learning and Control -- Connections and Perspectives).
@workshop{schulz2024learning,
title = {Learning When to Trust the Expert for Guided Exploration in RL},
author = {Felix Schulz and Jasper Hoffman and Yuan Zhang and Joschka Boedecker },
url = {https://openreview.net/forum?id=QkTANn4mRa},
year = {2024},
date = {2024-08-07},
urldate = {2025-08-07},
abstract = {Reinforcement learning (RL) algorithms often rely on trial and error for exploring environments, leading to local minima and high sample inefficiency during training. In many cases, leveraging prior knowledge can efficiently construct expert policies, e.g. model predictive control (MPC) techniques. However, the expert might not be optimal and thus, when used as a prior, might introduce bias that can harm the control performance. Thus, in this work, we propose a novel RL method based on a simple options framework that only uses the expert to guide the exploration during training. The exploration is controlled by a learned high-level policy that can decide to follow either an expert policy or a learned low-level policy. In that sense, the high-level skip policy learns when to trust the expert for exploration. As we aim at deploying the low-level policy without accessing the expert after training, we increasingly regularize the usage of the expert during training, to reduce the covariate shift problem. Using different environments combined with potentially sub-optimal experts derived from MPC or RL, we find that our method improves over sub-optimal experts and significantly improves the sample efficiency.},
note = {ICML 2024 Workshop: Foundations of Reinforcement Learning and Control -- Connections and Perspectives},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
Reinforcement learning (RL) algorithms often rely on trial and error for exploring environments, leading to local minima and high sample inefficiency during training. In many cases, leveraging prior knowledge can efficiently construct expert policies, e.g. model predictive control (MPC) techniques. However, the expert might not be optimal and thus, when used as a prior, might introduce bias that can harm the control performance. Thus, in this work, we propose a novel RL method based on a simple options framework that only uses the expert to guide the exploration during training. The exploration is controlled by a learned high-level policy that can decide to follow either an expert policy or a learned low-level policy. In that sense, the high-level skip policy learns when to trust the expert for exploration. As we aim at deploying the low-level policy without accessing the expert after training, we increasingly regularize the usage of the expert during training, to reduce the covariate shift problem. Using different environments combined with potentially sub-optimal experts derived from MPC or RL, we find that our method improves over sub-optimal experts and significantly improves the sample efficiency.
19.
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.
20.
Cecchin, Leonardo; Trachte, Adrian; Fagiano, Lorenzo; Diehl, Moritz
Real-time prediction of human-generated reference signals for advanced digging control Proceedings Article
In: pp. 496–501, IEEE, Bari, IT, 2024.
@inproceedings{cecchin_real-time_2024,
title = {Real-time prediction of human-generated reference signals for advanced digging control},
author = {Leonardo Cecchin and Adrian Trachte and Lorenzo Fagiano and Moritz Diehl},
doi = {10.1109/CASE59546.2024.10711371},
year = {2024},
date = {2024-08-01},
pages = {496–501},
publisher = {IEEE},
address = {Bari, IT},
abstract = {In the realm of excavator control, advanced techniques, such as Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback), proved to have great potential for enhancing efficiency and performance. These methods rely on the knowledge of future reference, which is often pre-defined, to optimize the system behavior as a function of it. However, this assumption fails in applications where a human operator chooses the reference at runtime, such as in the case of non-autonomous digging operations. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks. We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
In the realm of excavator control, advanced techniques, such as Model Predictive Control (MPC) and two-degrees-of-freedom structures (feedforward plus feedback), proved to have great potential for enhancing efficiency and performance. These methods rely on the knowledge of future reference, which is often pre-defined, to optimize the system behavior as a function of it. However, this assumption fails in applications where a human operator chooses the reference at runtime, such as in the case of non-autonomous digging operations. To cope with this problem, we study different approaches to use the collected data of human-generated reference signals to learn a predictive model of the operator commands. The considered methods are function approximation techniques based on Kriging, Set-Membership, and LSTM Neural Networks. We summarize the principles and the implementation of each method, and compare their performance using an experimental data-set of operations from a real-world excavator, where four operator-defined reference signals are predicted.