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Silvia Mastellone

Prof. Dr.
Silvia Mastellone
Equal Opportunity Chair
PI
FHNW
silvia.mastellone@fhnw.ch
Curriculum Vitae
I am passionate about control systems as a discipline that links the abstraction of elegant mathematics to engineering practice and technology.

Silvia Mastellone is Professor for Control and Signal Processing at the University of Applied Science Northwestern Switzerland. She obtained her PhD degree in Systems and Entrepreneurial Engineering from the University of Illinois at Urbana-Champaign in 2008. From 2008 to 2016 she was Principal Scientist at ABB Corporate Research Center in Switzerland, where she led research projects in the area of advanced control for energy systems. Her research interests include decentralized control and estimation and networked control systems, with applications in power conversion and energy systems. She is a member of the IFAC Industry Executive Committee and a member of the advisory board for the multiutility IBB.

Scientific Publications

Published
Reliability Study of an Adjustable Hybrid Switch (ahs) for a High Power Automotive Converter
IEEE Journal of Emerging and Selected Topics in Power Electronics
T. Koottungal, P. Reigosa, S. Mitrova, M. Rahimo, R. Minamisawa, Silvia Mastellone
Published
Enhancing Efficiency and Reliability of Electric Vehicles via Adaptive E-Gear Control
(ITCS) IEEE 26th International Conference on Intelligent Transportation Systems
L. Sandel, Gioele Zardini, S. Mitrova, T. Thekemuriyil, R. Minamisawa, M. Rahimo, Andrea Censi, Emilio Frazzoli, Silvia Mastellone
Doi.org Collection
Published
SOS Construction of Compatible Control Lyapunov and Barrier Functions
Proceedings of IFAC World congress
Michael Schneeberger, Florian Dörfler, Silvia Mastellone
Doi.org Collection
Published
Optimal control configuration in distribution network via an exact OPF relaxation method
2022 IEEE 61st Conference on Decision and Control (CDC)
Pages 5698-5704
Hossein Sekhavatmanesh, Giancarlo Ferrari-Trecate, Silvia Mastellone
Doi.org Collection
Published
Optimal Adaptive Droop Design via a Modified Relaxation of the OPF
IEEE Transactions on Control Systems Technology
Vol 31 No 2 Pages 497-510
Hossein Sekhavatmanesh, Giancarlo Ferrari-Trecate, Silvia Mastellone
Doi.org Collection
to Appear
Optimal droop control placement in distribution network via an exact OPF relaxation method
61st IEEE Conference on Decision and Control (CDC 2022)
Hossein Sekhavatmanesh, Giancarlo Ferrari-Trecate, Silvia Mastellone
Doi.org Collection

Research projects

Title
Principal Investigators

Layout optimization for decentralized operation of a network of power converters

Silvia Mastellone, Giancarlo Ferrari-Trecate

Summary

The project addresses the question of optimal system layout for facilitating decentralized control and optimal operation of a network of power converters with generators, storages and loads. Special emphasis is placed on the system capability of operating in a decentralized fashion to meet specific local control objectives (e.g. keeping the local voltage within prescribed limits) and system-wide control objectives (e.g. fair proportional load sharing among the inverters, provision of ancillary services). Sensitivity analysis will enable to define the boundaries of a completely decentralized structure and where adding a hierarchical level enables a significant performance improvement.

People involved

Silvia Mastellone, Giancarlo Ferrari-Trecate, Hossein Sekhavatmanesh
Layout optimization for decentralized operation of a network of power converters
Silvia Mastellone, Giancarlo Ferrari-Trecate

The project addresses the question of optimal system layout for facilitating decentralized control and optimal operation of a network of power converters with generators, storages and loads. Special emphasis is placed on the system capability of operating in a decentralized fashion to meet specific local control objectives (e.g. keeping the local voltage within prescribed limits) and system-wide control objectives (e.g. fair proportional load sharing among the inverters, provision of ancillary services). Sensitivity analysis will enable to define the boundaries of a completely decentralized structure and where adding a hierarchical level enables a significant performance improvement.

Silvia Mastellone, Giancarlo Ferrari-Trecate, Hossein Sekhavatmanesh
149
608284ed-f17e-4590-9f04-69f85324825e

Dependable Distributed and Hierarchical Control under Energy Constraints

Lothar Thiele, Colin Jones, Silvia Mastellone

Summary

We will investigate the theoretical and practical challenges of using energy harvesting to power nodes distributed control systems. Combining energy sources such as temperature and vibration with battery systems and wireless links enables the placement of sensor nodes where they are needed for the best data quality, regardless of the availability of wired power or communication. In the context of the NCCR, we will jointly investigate some of the open challenges in the design of autonomous, energy-neutral automation systems. In a second phase, we will investigate suitable demonstrators and applications such as wireless sensing and control in motor control and energy systems.

People involved

Colin Jones
Lothar Thiele, Colin Jones, Silvia Mastellone, Naomi Stricker
Dependable Distributed and Hierarchical Control under Energy Constraints
Lothar Thiele, Colin Jones, Silvia Mastellone

We will investigate the theoretical and practical challenges of using energy harvesting to power nodes distributed control systems. Combining energy sources such as temperature and vibration with battery systems and wireless links enables the placement of sensor nodes where they are needed for the best data quality, regardless of the availability of wired power or communication. In the context of the NCCR, we will jointly investigate some of the open challenges in the design of autonomous, energy-neutral automation systems. In a second phase, we will investigate suitable demonstrators and applications such as wireless sensing and control in motor control and energy systems.

Colin Jones
Lothar Thiele, Colin Jones, Silvia Mastellone, Naomi Stricker
116
64602cdc-7659-4ea1-a39f-1ded30d42b54

Learning Fast Convex Optimizers

Colin Jones, Daniel Kuhn, Silvia Mastellone

Summary

This project will study formal methods for reduced complexity design and verification of embedded optimization techniques for the control of high-speed nonlinear constrained systems. We will focus on machine learning techniques for differentiable parametric optimization and their application to the control of fast, nonlinear dynamic systems with a power conversion system taken as an important exemplar case study.

People involved

Colin Jones
Daniel Kuhn
Roland Schwan
Colin Jones, Daniel Kuhn, Silvia Mastellone, Roland Schwan
Learning Fast Convex Optimizers
Colin Jones, Daniel Kuhn, Silvia Mastellone

This project will study formal methods for reduced complexity design and verification of embedded optimization techniques for the control of high-speed nonlinear constrained systems. We will focus on machine learning techniques for differentiable parametric optimization and their application to the control of fast, nonlinear dynamic systems with a power conversion system taken as an important exemplar case study.

Colin Jones
Daniel Kuhn
Roland Schwan
Colin Jones, Daniel Kuhn, Silvia Mastellone, Roland Schwan
114
8a64bfd0-5a4a-4ae8-951a-bad586329d22

Data-driven control and estimation for the next generation of plug-and-play power converters

Silvia Mastellone, Florian Dörfler

Summary

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

People involved

Florian Dörfler
Michael Schneeberger
Silvia Mastellone, Florian Dörfler, Michael Schneeberger
Data-driven control and estimation for the next generation of plug-and-play power converters
Silvia Mastellone, Florian Dörfler

The project aims at developing an autonomous and reconfigurable control architecture where each converter operates in plug-and-play mode, i.e., without additional communication overhead or detailed model knowledge, and under variable system conditions. This requires in a first step that each converter estimates online the equivalent circuit as seen at its terminals. Based on such estimates, the converter controller reconfigures its structure and adapts online its parameters in order to optimally perform under the different grid configurations and operating conditions.

Florian Dörfler
Michael Schneeberger
Silvia Mastellone, Florian Dörfler, Michael Schneeberger
104
de9d6331-cbcb-4b12-b9e4-9c4f6fe448c9

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