PhD Candidate
Knowledge Engineering & Digital Twin Technologies
Robert Bosch GmbH, Corporate Research
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, 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”.
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.
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.
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.
4.
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.
5.
Cecchin, Leonardo; Ohtsuka, Toshiyuki; Trachte, Adrian; Diehl, Moritz
Model Predictive Controller for Hydraulic Cylinders with Independent Metering Control Valves Proceedings Article
In: IFAC-PapersOnLine, pp. 281–287, IFAC, Kyoto, JP, 2024.
@inproceedings{cecchin_model_2024,
title = {Model Predictive Controller for Hydraulic Cylinders with Independent Metering Control Valves},
author = {Leonardo Cecchin and Toshiyuki Ohtsuka and Adrian Trachte and Moritz Diehl},
url = {https://www.sciencedirect.com/science/article/pii/S240589632401423X},
doi = {10.1016/j.ifacol.2024.09.044},
year = {2024},
date = {2024-08-01},
booktitle = {IFAC-PapersOnLine},
volume = {58},
pages = {281–287},
publisher = {IFAC},
address = {Kyoto, JP},
series = {18},
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 (acados). 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.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
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 (acados). 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.
6.
Cecchin, Leonardo; Frey, Jonathan; Gering, Stefan; Manderla, Maximilian; Trachte, Adrian; Diehl, Moritz
Nonlinear Model Predictive Control for Efficient Control of Variable Speed Variable Displacement Pumps Proceedings Article
In: 2023 Modeling, Estimation and Control Conference (MECC), pp. 1–6, IFAC 2023.
@inproceedings{cecchin2023nonlinear,
title = {Nonlinear Model Predictive Control for Efficient Control of Variable Speed Variable Displacement Pumps},
author = { Leonardo Cecchin and Jonathan Frey and Stefan Gering and Maximilian Manderla and Adrian Trachte and Moritz Diehl},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 Modeling, Estimation and Control Conference (MECC)},
pages = {1–6},
organization = {IFAC},
abstract = {Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Hydraulic pumps are a key component in manufacturing industry and off-highway vehicles.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.
Paired with diesel engines or electric motors, they provide hydraulic flow that can conveniently be used to power a variety of actuators.
Hydraulic power transmission has numerous advantages, unfortunately energy efficiency is usually not one of those.
The use of Variable Speed Variable Displacement pumps has been proven to be advantageous with respect to constant speed or constant displacement solutions: It allows to achieve higher efficiency and faster flow tracking dynamics.
This paper presents the development of a Model Predictive Control for this system, considering the nonlinearities and look-up-tables that characterize the system dynamics.
The Model Predictive Controller is then compared both in simulation and on test bench with a reference controller for such system, showing potential both regarding efficiency and flow tracking dynamics.
7.
Cecchin, Leonardo; Baumgärtner, Katrin; Gering, Stefan; Diehl, Moritz
Locally Weighted Regression with Approximate Derivatives for Data-based optimization Proceedings Article
In: 2023 European Control Conference (ECC), pp. 1–6, IEEE 2023.
@inproceedings{cecchin2023locally,
title = {Locally Weighted Regression with Approximate Derivatives for Data-based optimization},
author = { Leonardo Cecchin and Katrin Baumgärtner and Stefan Gering and Moritz Diehl},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2023 European Control Conference (ECC)},
pages = {1–6},
organization = {IEEE},
abstract = {Interpolation and approximation of data provided in terms of a Look-Up Table (LUT) is a common and well-known task, and is especially relevant for industrial applications. When using the function for point-wise evaluation, the method choice only affects the accuracy of the function value itself. However, when the LUT is used as part of an optimization problem formulation, a bad method choice can prevent convergence or alter significantly the outcome of the solver. Moreover, computational efficiency becomes critical due to the much higher number of evaluations required. This work focuses on a variation of Locally Weighted Regression, with approximate derivatives computation. The result is a method that allows one to obtain the function value together with the first n derivatives, at a reduced computational cost. Theoretical properties of the approach are analyzed, and the results of a minimization problem using the proposed method are compared with more traditional ones. The new approach shows promising performance and results, both for computational efficiency and effectiveness when used in optimization.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Interpolation and approximation of data provided in terms of a Look-Up Table (LUT) is a common and well-known task, and is especially relevant for industrial applications. When using the function for point-wise evaluation, the method choice only affects the accuracy of the function value itself. However, when the LUT is used as part of an optimization problem formulation, a bad method choice can prevent convergence or alter significantly the outcome of the solver. Moreover, computational efficiency becomes critical due to the much higher number of evaluations required. This work focuses on a variation of Locally Weighted Regression, with approximate derivatives computation. The result is a method that allows one to obtain the function value together with the first n derivatives, at a reduced computational cost. Theoretical properties of the approach are analyzed, and the results of a minimization problem using the proposed method are compared with more traditional ones. The new approach shows promising performance and results, both for computational efficiency and effectiveness when used in optimization.
8.
Saccani, Danilo; Cecchin, Leonardo; Fagiano, Lorenzo
Multitrajectory Model Predictive Control for Safe UAV Navigation in an Unknown Environment Journal Article
In: IEEE Transactions on Control Systems Technology, pp. 1-16, 2022.
@article{9938397,
title = {Multitrajectory Model Predictive Control for Safe UAV Navigation in an Unknown Environment},
author = {Danilo Saccani and Leonardo Cecchin and Lorenzo Fagiano},
doi = {10.1109/TCST.2022.3216989},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {IEEE Transactions on Control Systems Technology},
pages = {1-16},
abstract = {The problem of navigating an unmanned aerial vehicle (UAV) in an unknown environment is addressed with a novel model predictive control (MPC) formulation, named multitrajectory MPC (mt-MPC). The objective is to safely drive the vehicle to the desired target location by relying only on the partial description of the surroundings provided by an exteroceptive sensor. This information results in time-varying constraints during the navigation among obstacles. The proposed mt-MPC generates a sequence of position set points that are fed to control loops at lower hierarchical levels. To do so, the mt-MPC predicts two different state trajectories, a safe one and an exploiting one, in the same finite horizon optimal control problem (FHOCP). This formulation, particularly suitable for problems with uncertain time-varying constraints, allows one to partially decouple constraint satisfaction (safety) from cost function minimization (exploitation). Uncertainty due to modeling errors and sensors noise is taken into account as well, in a set membership (SM) framework. Theoretical guarantees of persistent obstacle avoidance are derived under suitable assumptions, and the approach is demonstrated experimentally out-of-the-laboratory on a prototype built with off-the-shelf components.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The problem of navigating an unmanned aerial vehicle (UAV) in an unknown environment is addressed with a novel model predictive control (MPC) formulation, named multitrajectory MPC (mt-MPC). The objective is to safely drive the vehicle to the desired target location by relying only on the partial description of the surroundings provided by an exteroceptive sensor. This information results in time-varying constraints during the navigation among obstacles. The proposed mt-MPC generates a sequence of position set points that are fed to control loops at lower hierarchical levels. To do so, the mt-MPC predicts two different state trajectories, a safe one and an exploiting one, in the same finite horizon optimal control problem (FHOCP). This formulation, particularly suitable for problems with uncertain time-varying constraints, allows one to partially decouple constraint satisfaction (safety) from cost function minimization (exploitation). Uncertainty due to modeling errors and sensors noise is taken into account as well, in a set membership (SM) framework. Theoretical guarantees of persistent obstacle avoidance are derived under suitable assumptions, and the approach is demonstrated experimentally out-of-the-laboratory on a prototype built with off-the-shelf components.