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”. 

Project description

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.



Cecchin, Leonardo; Ohtsuka, Toshiyuki; Trachte, Adrian; Diehl, Moritz

Model Predictive Controller for Hydraulic Cylinders with Independent Metering Control Valves Working paper

2024, (Submitted to the 2024 IFAC Conference on Nonlinear Model Predictive Control (NMPC)).

Abstract | BibTeX


Cecchin, Leonardo; Trachte, Adrian; Fagiano, Lorenzo; Diehl, Moritz

Real-time prediction of human-generated reference signals: a case study in advanced digging control Working paper

2024, (Submitted to the 2024 European Control Conference (ECC)).

Abstract | BibTeX


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.

Abstract | BibTeX


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.

Abstract | BibTeX


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.

Abstract | Links | BibTeX