Publications related to First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

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

In this article, we propose an approach to extract variable-impedance during cutting tasks from human demonstrations, so as to ease soft-tissue cutting by robots. We model the dynamic adjustment of the human arm during interactions with the tissue and tran ...

2021

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

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Marco Matter

In daily adaptive proton therapy (DAPT), the treatment plan is re-optimized on a daily basis. It is a straightforward idea to incorporate information from the previous deliveries during the optimization to refine this daily proton delivery. A feedback sign ...

2020

Low-Complexity Optimization-Based Control: Design, Methods and Applications

Ivan Pejcic

Optimization-based controllers are advanced control systems whose mechanism of determining control inputs requires the solution of a mathematical optimization problem. In this thesis, several contributions related to the computational effort required for o ...

EPFL2019

Kernel methods and Model predictive approaches for Learning and Control

Sanket Sanjay Diwale

Data-driven modeling and feedback control play a vital role in several application areas ranging from robotics, control theory, manufacturing to management of assets, financial portfolios and supply chains. Many such problems in one way or another are rela ...

EPFL2019

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

Alireza Karimi, Achille Nicoletti

In this paper, a new data-driven method for designing robust controllers is proposed for systemswith sector-bounded nonlinearities and multimodel uncertainties. The results from the circle criterion are used to generate necessary and sufficient convex cons ...

WILEY2019

An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis

Maryam Kamgarpour, Luca Furieri

This letter proposes a novel input-output parametrization of the set of internally stabilizing output-feedback controllers for linear time invariant (LTI) systems. Our underlying idea is to directly treat the closed-loop transfer matrices from disturbances ...

2019

A New Time-Varying Feedback RISE Control of PKMs: Theory and Application

Mohamed Bouri

In this paper, we propose a novel time-varying feedback control strategy based on the Robust Integral of the Sign of the Error (RISE). The main motivation is to enhance the tracking performance of RISE controller at high dynamic operating conditions. RISE ...

IEEE2019

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Achille Nicoletti

The objective of this dissertation is to develop data-driven frequency-domain methods for designing robust controllers through the use of convex optimization algorithms. Many of today's industrial processes are becoming more complex, and modeling accurate ...

EPFL2018

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Alireza Karimi, Achille Nicoletti

Recently, a new data-driven method for robust control with H-infinity performance has been proposed. This method is based on convex optimization and converges to the optimal performance when the controller order increases. However, for low-order controller ...

IEEE2018

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

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

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

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

Low-Complexity Optimization-Based Control: Design, Methods and Applications

Kernel methods and Model predictive approaches for Learning and Control

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis

A New Time-Varying Feedback RISE Control of PKMs: Theory and Application

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

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

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

Update on yesterday's dose-Use of delivery log-files for daily adaptive proton therapy (DAPT)

A New Time-Varying Feedback RISE Control of PKMs: Theory and Application

Kernel methods and Model predictive approaches for Learning and Control

A Data-Driven Method for Computing Fixed-Structure Low-Order Controllers With H-infinity Performance

Low-Complexity Optimization-Based Control: Design, Methods and Applications

A Data-Driven Frequency-Domain Approach for Robust Controller Design via Convex Optimization

Robust control of systems with sector nonlinearities via convex optimization: A data-driven approach

An Input–Output Parametrization of Stabilizing Controllers: Amidst Youla and System Level Synthesis