{"id":651,"date":"2021-09-25T19:17:30","date_gmt":"2021-09-25T19:17:30","guid":{"rendered":"https:\/\/elo-x.eu\/?p=651"},"modified":"2023-09-20T17:05:29","modified_gmt":"2023-09-20T17:05:29","slug":"leonardo-cecchin","status":"publish","type":"post","link":"https:\/\/elo-x.eu\/?p=651","title":{"rendered":"Leonardo Cecchin"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\n\t\t
\n\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t

\n\t\t\t\t\t\t\t\t\n\t\t\t\tLeonardo Cecchin \n\t\t\t<\/h2 > \n\t\t\t\t\t\t\t\t\t<\/a>\n\t\t\t\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

PhD Candidate<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
\n

Knowledge Engineering & Digital Twin Technologies<\/b><\/span><\/p>\n

Robert Bosch GmbH, Corporate Research<\/b><\/span> <\/span><\/p>\n<\/div>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\"\"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t
<\/div>\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Leonardo Cecchin graduated in Automation and Control Engineering at Politecnico di Milano in October 2020, with the thesis “Graph-Based Exploration and Mapping for Mobile Robots”. From November 2020 until June 2021 he has been a Research Fellow at SAS-Lab<\/a>, working on graph-based approaches for exploration and mapping with autonomous multicopter drones, equipped with positioning systems and LiDAR turrets. Since July 2021 he is carrying out a PhD at Bosch Research, Germany, in the framework of the Marie Curie Initial Training Network “ELO-X”. <\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Project description<\/a><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Hydraulic systems are integral to our modern society, powering a wide range of applications, from industrial machinery to construction equipment. Their efficiency is vital in conserving energy resources. To address this, the project centers on the creation of an Adaptive Multilayer Model Predictive Controller for Hydraulic systems. It entails modeling of the system, the exploration of diverse control strategies across system components, and the holistic assessment of the controller’s architecture. The controller’s performance is rigorously tested through simulation, benchmarked against alternative approaches, and ultimately validated in real-world experimental settings.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t
\n\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tRead more about this project<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
\n\t\t\t\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t

Publications<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t
<\/a>
<\/div><\/div><\/form>
1.<\/div>

Gottardini, Andrea; Cecchin, Leonardo; Demir, Ozan; Fagiano, Lorenzo<\/p>

Data-Driven Nonlinear Model Predictive Control for Grading Functions for Excavators Working paper<\/span> Forthcoming<\/span><\/p>

Forthcoming.<\/p>

Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@workingpaper{gottardini_data-driven_2025,
\r\ntitle = {Data-Driven Nonlinear Model Predictive Control for Grading Functions for Excavators},
\r\nauthor = {Andrea Gottardini and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
\r\nyear  = {2025},
\r\ndate = {2025-12-31},
\r\nabstract = {Hydraulic excavators are essential construc-
\r\ntion machines widely utilized for ground shaping tasks,
\r\nsuch as horizontal leveling and creating sloped surfaces.
\r\nThese operations require a high degree of precision, which
\r\ncan be challenging for unskilled workers.
\r\nThe implementation of automation in hydraulic machin-
\r\nery has the potential to significantly enhance productivity
\r\nby improving accuracy and reducing reliance on highly
\r\ntrained labor. However, the control of hydraulic systems is
\r\ncomplicated by strong nonlinearities and variability among
\r\nmachines, making the design of effective controllers a sig-
\r\nnificant challenge.
\r\nIn this paper, we propose a data-driven Model Predictive
\r\nControl (MPC) system, initially developed for trajectory
\r\ntracking and subsequently adapted for a path following
\r\napproach. This adaptation is crucial because the trajectory
\r\ntracking method relies on open-loop references, using a
\r\npredefined speed profile that does not account for the
\r\ndynamics of the hydraulic excavator, potentially leading to
\r\ndifficult-to-follow trajectories.
\r\nThe prediction model used in the MPC is based on Linear
\r\nLocal Neuro-Fuzzy Models, trained with the LOcal LInear
\r\nMOdel Tree (LOLIMOT) algorithm, while the linear parame-
\r\nters are refined using the Simulation Error Method (SEM).
\r\nThe proposed control system was rigorously tested on
\r\na JCB Hydradig 110W following a comprehensive data
\r\ncollection campaign to obtain the necessary data for the
\r\ndata-driven model of the hydraulic cylinders.
\r\nResults, evaluated using metrics such as root mean
\r\nsquare error (RMSE) between the actual and reference
\r\npaths, maximum error, and standard deviation (indicating
\r\noscillations during motion), demonstrate that our approach
\r\noutperforms previous data-driven feed-forward controllers,
\r\nhighlighting its efficacy in enhancing hydraulic automation.},
\r\nkeywords = {},
\r\npubstate = {forthcoming},
\r\ntppubtype = {workingpaper}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
Hydraulic excavators are essential construc-
\r\ntion machines widely utilized for ground shaping tasks,
\r\nsuch as horizontal leveling and creating sloped surfaces.
\r\nThese operations require a high degree of precision, which
\r\ncan be challenging for unskilled workers.
\r\nThe implementation of automation in hydraulic machin-
\r\nery has the potential to significantly enhance productivity
\r\nby improving accuracy and reducing reliance on highly
\r\ntrained labor. However, the control of hydraulic systems is
\r\ncomplicated by strong nonlinearities and variability among
\r\nmachines, making the design of effective controllers a sig-
\r\nnificant challenge.
\r\nIn this paper, we propose a data-driven Model Predictive
\r\nControl (MPC) system, initially developed for trajectory
\r\ntracking and subsequently adapted for a path following
\r\napproach. This adaptation is crucial because the trajectory
\r\ntracking method relies on open-loop references, using a
\r\npredefined speed profile that does not account for the
\r\ndynamics of the hydraulic excavator, potentially leading to
\r\ndifficult-to-follow trajectories.
\r\nThe prediction model used in the MPC is based on Linear
\r\nLocal Neuro-Fuzzy Models, trained with the LOcal LInear
\r\nMOdel Tree (LOLIMOT) algorithm, while the linear parame-
\r\nters are refined using the Simulation Error Method (SEM).
\r\nThe proposed control system was rigorously tested on
\r\na JCB Hydradig 110W following a comprehensive data
\r\ncollection campaign to obtain the necessary data for the
\r\ndata-driven model of the hydraulic cylinders.
\r\nResults, evaluated using metrics such as root mean
\r\nsquare error (RMSE) between the actual and reference
\r\npaths, maximum error, and standard deviation (indicating
\r\noscillations during motion), demonstrate that our approach
\r\noutperforms previous data-driven feed-forward controllers,
\r\nhighlighting its efficacy in enhancing hydraulic automation.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
2.<\/div>
 Msaad, Salim;  Cecchin, Leonardo;  Demir, Ozan;  Fagiano, Lorenzo<\/p>
Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks Proceedings Article<\/span> Forthcoming<\/span><\/p>
In: <\/span>2025 American Control Conference (ACC), <\/span>Forthcoming.<\/p>
Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@inproceedings{msaad_data-driven_2025,
\r\ntitle = {Data-Driven Model Predictive Control of an Hydraulic Excavator via Local Model Networks},
\r\nauthor = {Salim Msaad and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
\r\nyear  = {2025},
\r\ndate = {2025-07-01},
\r\nurldate = {2025-07-01},
\r\npublisher = {2025 American Control Conference (ACC)},
\r\nabstract = {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\u2019s 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\u2019 pressure measurement.},
\r\nkeywords = {},
\r\npubstate = {forthcoming},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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\u2019s 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\u2019 pressure measurement.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
3.<\/div>
 Cupo, Alessandro;  Cecchin, Leonardo;  Demir, Ozan;  Fagiano, Lorenzo<\/p>
Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators Proceedings Article<\/span> <\/p>
In: <\/span>4th Modeling, Estimation and Control Conference (MECC), <\/span>2024<\/span>.<\/p>
Abstract<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@inproceedings{cupo_energy-optimal_2024,
\r\ntitle = {Energy-Optimal Trajectory Planning for Semi-Autonomous Hydraulic Excavators},
\r\nauthor = {Alessandro Cupo and Leonardo Cecchin and Ozan Demir and Lorenzo Fagiano},
\r\nyear  = {2024},
\r\ndate = {2024-10-01},
\r\npublisher = {4th Modeling, Estimation and Control Conference (MECC)},
\r\nabstract = {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\u2019s 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\u2019s choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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\u2019s 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\u2019s choice. Lastly, the problem is extended to include obstacle-avoidance constraints, creating a collision-free and efficient path.<\/div>
Close<\/a><\/p><\/div><\/div><\/div>
4.<\/div>
 Cecchin, Leonardo;  Trachte, Adrian;  Fagiano, Lorenzo;  Diehl, Moritz<\/p>
Real-time prediction of human-generated reference signals for advanced digging control<\/a> Proceedings Article<\/span> <\/p>
In: <\/span>pp. 496\u2013501, <\/span>IEEE, <\/span>Bari, IT, <\/span>2024<\/span>.<\/p>
Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>
@inproceedings{cecchin_real-time_2024,
\r\ntitle = {Real-time prediction of human-generated reference signals for advanced digging control},
\r\nauthor = {Leonardo Cecchin and Adrian Trachte and Lorenzo Fagiano and Moritz Diehl},
\r\ndoi = {10.1109\/CASE59546.2024.10711371},
\r\nyear  = {2024},
\r\ndate = {2024-08-01},
\r\npages = {496\u2013501},
\r\npublisher = {IEEE},
\r\naddress = {Bari, IT},
\r\nabstract = {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 ef\ufb01ciency and performance. These methods rely on the knowledge of future reference, which is often pre-de\ufb01ned, 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-de\ufb01ned reference signals are predicted.},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {inproceedings}
\r\n}
\r\n<\/pre><\/div>
Close<\/a><\/p><\/div>
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 ef\ufb01ciency and performance. These methods rely on the knowledge of future reference, which is often pre-de\ufb01ned, 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-de\ufb01ned reference signals are predicted.<\/div>
Close<\/a><\/p><\/div>