Publications related to Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

This paper introduces a novel method for data-driven robust control of nonlinear systems based on the Koopman operator, utilizing Integral Quadratic Constraints (IQCs). The Koopman operator theory facilitates the linear representation of nonlinear system d ...

2024

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Aude Billard, Bernardo Fichera

Dynamical System (DS)-based closed-loop control is a simple and effective way to generate reactive motion policies that well generalize to the robotic workspace, while retaining stability guarantees. Lately the formalism has been expanded in order to handl ...

SPRINGER INTERNATIONAL PUBLISHING AG2023

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

Giancarlo Ferrari Trecate, John Lygeros, Luca Furieri, Florian Dörfler, Andrea Martin

We consider control of dynamical systems through the lens of competitive analysis. Most prior work in this area focuses on minimizing regret, that is, the loss relative to an ideal clairvoyant policy that has noncausal access to past, present, and future d ...

Elsevier2023

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Amir Aminifar

Editor's notes: The cross-layer approach presented in this article involves co-designing a feedback controller's parameters together with the schedule of an Ethernet network used for communicating state information and control signals.-Samarjit Chakraborty ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021

Probabilistic Adaptive Control for Robust Behavior Imitation

Sylvain Calinon, Julius Maximilian Jankowski, Hakan Girgin

In the context of learning from demonstration (LfD), trajectory policy representations such as probabilistic movement primitives (ProMPs) allow for rich modeling of demonstrated skills. To reproduce a learned skill with a real robot, a feedback controller ...

IEEE2021

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

Maryam Kamgarpour, Luca Furieri

We study the distributed Linear Quadratic Gaussian (LQG) control problem in discrete-time and finite-horizon, where the controller depends linearly on the history of the outputs and it is required to lie in a given subspace, e.g. to possess a certain spars ...

IEEE2020

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Nikolaos Geroliminis, Isik Ilber Sirmatel, Ye Ren

Most perimeter control methods in literature are the model-based schemes designing the controller based on the available accurate macroscopic fundamental diagram (MFD) function with well known techniques of modern control methods. However, accurate modelin ...

PERGAMON-ELSEVIER SCIENCE LTD2020

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Maryam Kamgarpour, Luca Furieri, Na Li

It is known that the set of internally stabilizing controller

\mathcal{C}_{\text{stab}}

is non-convex, but it admits convex characterizations using certain closed-loop maps: a classical result is the {Youla parameterization}, and two recent notions are t ...

2020

Model-based real-time plasma electron density profile estimation and control on ASDEX Upgrade and TCV

Federico Alberto Alfredo Felici, Cristian Galperti

Real-time plasma electron density profile estimation and control are essential in the operation of future tokamaks. In particular, the robustness against diagnostics failure and disturbances is important for long pulse operation. A model-based approach to ...

ELSEVIER SCIENCE SA2019

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Diogo Filipe Mateus Rodrigues

The concept of reaction variants and invariants for lumped reaction systems has been known for several decades. Its applications encompass model identification, data reconciliation, state estimation and control using kinetic models. In this thesis, the con ...

EPFL2018

Learning From Demonstration and Interactive Control of Variable-Impedance to Cut Soft Tissues

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Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Probabilistic Adaptive Control for Robust Behavior Imitation

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation

Model-based real-time plasma electron density profile estimation and control on ASDEX Upgrade and TCV

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Koopman-based Data-driven Robust Control of Nonlinear Systems Using Integral Quadratic Constraints

Hybrid Quadratic Programming - Pullback Bundle Dynamical Systems Control

Concept of Variants and Invariants for Reaction Systems, with Application to Estimation, Control and Optimization

Probabilistic Adaptive Control for Robust Behavior Imitation

Model-based real-time plasma electron density profile estimation and control on ASDEX Upgrade and TCV

Breaking Silos to Guarantee Control Stability with Communication over Ethernet TSN

Data driven model free adaptive iterative learning perimeter control for large-scale urban road networks

Follow the Clairvoyant: an Imitation Learning Approach to Optimal Control

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

System-level, Input-output and New Parameterizations of Stabilizing Controllers, and Their Numerical Computation