Publication

Optimal Control for Real-Time Feedback Rate-Monotonic Schedulers

Publications associées (33)

Data-driven Methods for Control: from Linear to Lifting

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The progress towards intelligent systems and digitalization relies heavily on the use of automation technology. However, the growing diversity of control objects presents significant challenges for traditional control approaches, as they are highly depende ...
EPFL2023

Real-Time Nonlinear Model Predictive Control for Fast Mechatronic Systems

Petr Listov

This thesis presents an efficient and extensible numerical software framework for real-time model-based control. We are motivated by complex and challenging mechatronic applications spanning from flight control of fixed-wing aircraft and thrust vector cont ...
EPFL2022

Optimal Thrust Vector Control of an Electric Small-Scale Rocket Prototype

Colin Neil Jones, Roland Schwan, Petr Listov

Recent advances in Model Predictive Control (MPC) algorithms and methodologies, combined with the surge of computational power of available embedded platforms, allows the use of real-time optimization-based control of fast mechatronic systems. This paper p ...
2021

Whole Body Model Predictive Control with a Memory of Motion:Experiments on a Torque-Controlled Talos

Sylvain Calinon, Teguh Santoso Lembono, Rohan Budhiraja

This paper presents the first successful experiment implementing whole-body model predictive control with state feedback on a torque-control humanoid robot. We demonstrate that our control scheme is able to do whole-body target tracking, control the balanc ...
IEEE2021

Improving sound absorption through nonlinear active electroacoustic resonators

Romain Christophe Rémy Fleury, Hervé Lissek, Xinxin Guo

Absorbing airborne noise at frequencies below 300 Hz is a particularly vexing problem due to the absence of natural sound absorbing materials at these frequencies. The prevailing solution for low-frequency sound absorption is the use of passive narrow-band ...
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

Model-based predictive control methods for distributed energy resources in smart grids.

Luca Fabietti

This thesis develops optimization-based techniques for the control of distributed energy resources to provide multiple services to the power network. It is divided into three parts. The first part of this thesis focuses on the development of a framework f ...
EPFL2019

Input-Constrained Path Following for Autonomous Marine Vehicles with a Global Region of Attraction

Francisco Fernandes Castro Rego

This paper presents a solution to the problem of path following control for autonomous marine vehicles (AMVs) subject to input constraints and constant ocean current disturbances. We propose two nonlinear control strategies: the first is obtained by using ...
ELSEVIER SCIENCE BV2018

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