Dependable Control and Decision group

DECODE – Dependable Control and Decision group

The DECODE (Dependable Control and Decision) group focuses on complex control systems, where coupling between subsystems, local sources of uncertainty, and structural changes can undermine reliability and generate dangerous behaviors. By leveraging tools from control theory, optimization, and machine learning, we contribute to the development of next-generation systems that are dependable, more autonomous and self adapt to changes in the internal dynamics and the surrounding environment.

The DECODE group is part of the Automatic Control Laboratory and the NCCR automation.

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Research topics

Control of large-scale systems
Microgrids with flexible structure
Data-based control

News

Schematic of a linear interconnected multi-agent system © M. S. Turan 2021 EPFL

New publication on IEEE Transactions on Control of Network Systems

— Our paper "On Consensusability of Linear Interconnected Multi-Agent Systems and Simultaneous Stabilization" has been published on IEEE Transactions on Control of Network Systems (TCNS). The online version of the paper can be found here.

© 2021 EPFL

New paper on IEEE Control Systems Letters!

— Our paper "Data-driven Unknown-input Observers and State Estimation" has been published on the IEEE Control System Letters (L-CSS). The article can be found here.

© 2021 EPFL

Invited Presentation at ETHZ IfA Coffee Talk Seminar

— Dr. Liang Xu was invited to give a presentation about our recent work "A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design" at ETHZ IfA Coffee Talk Seminar on July 22nd, 2021.

© 2021 EPFL

New technical report on Bayesian identification of Power Networks

— Bayesian error-in-variable models made specifically for distribution grids can enable the automatic estimation of network parameters from noisy phasor measurements.

© 2021 EPFL

IEEE Switzerland: Foundation of the CSS chapter

— On May 4th, 2021 the chapter of the Control Systems Society of the IEEE Switzerland Section has been formed. The chapter activities will be coordinated by Giancarlo Ferrari Trecate (founder and chapter chair) and Melanie Zeilinger (chapter co-chair).

© 2021 EPFL

Performance and safety guarantees from NOISY data! *New Preprint*

— Solely using noisy historical data, we synthesize a robustly safe output-feedback policy, for which the suboptimality and safety gaps are explicitly and tightly upperbounded in terms of the level of corrupting noise. This is achieved by developing a new tractable optimal control method, the Behavioral Input-Output Parametrization (BIOP), which first exploits behavioral theory to identify a precise impulse response, and then sets up a quasi-convex optimal control procedure with guaranteed high performance.  Check out our preprint! 

State predictions converge to the real states © 2021 EPFL

State estimation using data: we have a new preprint on arXiv!

— Our new technical report "Data-driven Unknown-input Observers and State Estimation" leverages the recently developed behavioral system theory to propose a completely data-driven framework for unknown-input observers and regular state estimation. 

Outputs are regulated to around zero for all noise realizations © 2021 EPFL

Our technical report on data-driven control is out

— The paper, entitled "A Data-Driven Convex Programming Approach to Worst-Case Robust Tracking Controller Design", uses behavioral system theory to provide a convex-programming-based framework for solving worst-case optimal tracking problems in a data-driven fashion. It also guarantees that input/output constraints are always satisfied when the output data are noisy and inputs have disturbances.

Poster © EPFL 2021

We present our poster in L4DC Conference 2021

— Our paper on data-driven robust optimal control has been accepted to the 3rd Annual Learning for Dynamics and Control (L4DC) Conference. It will be virtually presented on June 7, 2021.

All news

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Funding acknowledgment

Contact

Head of the group: Giancarlo Ferrari Trecate
Office: ME C2 398
Mail: [email protected]

Administration : Nicole Bouendin
Office : ME C2 389
Phone : +41 21 693 38 45
Fax : +41 21 693 2574
Mail : [email protected]


Dependable Control and Decision group (DECODE)
Laboratoire d’Automatique
Ecole Polytechnique Fédérale de Lausanne
EPFL STI IGM SCI-STI-GFT 
ME C2 398 (Bâtiment ME) , Station 9 
CH-1015 Lausanne
Switzerland