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Concept

Stochastic control

Publications associées (29)

An Iterative Adaptive Dynamic Programming Approach for Macroscopic Fundamental Diagram-Based Perimeter Control and Route Guidance

Nikolaos Geroliminis

Macroscopic fundamental diagrams (MFDs) have been widely adopted to model the traffic flow of large-scale urban networks. Coupling perimeter control and regional route guidance (PCRG) is a promising strategy to decrease congestion heterogeneity and reduce ...

Informs2024

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

Alireza Karimi, Mert Eyuboglu

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

Homothetic Tube Model Predictive Control With Multi-Step Predictors

Giancarlo Ferrari Trecate, Melanie Nicole Zeilinger, Danilo Saccani

We present a robust model predictive control (MPC) framework for linear systems facing bounded parametric uncertainty and bounded disturbances. Our approach deviates from standard MPC formulations by integrating multi-step predictors, which provide reduced ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Training Efficient Controllers via Analytic Policy Gradient

Dario Floreano, Davide Scaramuzza, Valentin Wüest

Control design for robotic systems is complex and often requires solving an optimization to follow a trajectory accurately. Online optimization approaches like Model Predictive Control (MPC) have been shown to achieve great tracking performance, but requir ...

New York2023

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

Robust optimization of control parameters for WEC arrays using stochastic methods

Tommaso Vanzan

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients ...

Journal of Computational Physics2023

Distributionally Robust Linear Quadratic Control

Daniel Kuhn, Bahar Taskesen, Cagil Kocyigit

Linear-Quadratic-Gaussian (LQG) control is a fundamental control paradigm that is studied in various fields such as engineering, computer science, economics, and neuroscience. It involves controlling a system with linear dynamics and imperfect observations ...

2023

Reinforcement Learning for Joint Design and Control of Battery-PV Systems

Christophe Ballif, Nicolas Würsch, Laure-Emmanuelle Perret Aebi, Marine Dominique Cauz

The decentralisation and unpredictability of new renewable energy sources require rethinking our energy system. Data-driven approaches, such as reinforcement learning (RL), have emerged as new control strategies for operating these systems, but they have n ...

2023

A Proximal-Point Lagrangian-Based Parallelizable Nonconvex Solver for Bilinear Model Predictive Control

Colin Neil Jones, Yuning Jiang, Yingzhao Lian

Nonlinear model predictive control (NMPC) has been widely adopted to manipulate bilinear systems with dynamics that include products of the inputs and the states. These systems are ubiquitous in chemical processes, mechanical systems, and quantum physics, ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Model Predictive Control for Multi-Agent Systems under Limited Communication and Time-Varying Network Topology

Melanie Nicole Zeilinger, Danilo Saccani

In control system networks, reconfiguration of the controller when agents are leaving or joining the network is still an open challenge, in particular when operation constraints that depend on each agent's behavior must be met. Drawing our motivation from ...

IEEE2023

Achieving Optimal Performance With Data-Driven Frequency-Based Control Synthesis Methods

Alireza Karimi, Philippe Louis Schuchert

Frequency Response Function (FRF)-based control synthesis methods for Linear Time-Invariant (LTI) systems have been widely used in control theory and industry. Recently, there has been renewed interest in these methods, employing numerical optimization too ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

José del Rocio Millán Ruiz, Aude Billard, Fumiaki Iwane

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varie ...

Nature Portfolio2023

Direct Arm Energy Control of the Modular Multilevel Matrix Converter

Drazen Dujic, Milan Utvic, Philippe Alexandre Bontemps

Modular multilevel matrix converter is a type of converter used in medium-voltage ac-ac power conversion. Controlling the energy content of the converter arms is a prerequisite for a proper converter operation, and at the same time a challenging control ta ...

2023

Data-driven modeling of beam loss in the LHC

Tatiana Pieloni, Michael Schenk, Ekaterina Krymova, Loïc Thomas Davies Coyle

In the Large Hadron Collider, the beam losses are continuously measured for machine protection. By design, most of the particle losses occur in the collimation system, where the particles with high oscilla-tion amplitudes or large momentum error are scrape ...

FRONTIERS MEDIA SA2023

Optimized numerical solutions of SIRDVW multiage model controlling SARS-CoV-2 vaccine roll out: An application to the Italian scenario

Alfio Quarteroni, Luca Dede', Nicola Parolini

In the context of SARS-CoV-2 pandemic, mathematical modelling has played a funda-mental role for making forecasts, simulating scenarios and evaluating the impact of pre-ventive political, social and pharmaceutical measures. Optimal control theory represent ...

KEAI PUBLISHING LTD2023

Indentation and stability of woven domes

Pedro Miguel Nunes Pereira de Almeida Reis, Tian Chen, Samuel Jean Bernard Poincloux, Tomohiko Sano, Célestin Vallat

Discrete domes are doubly curved structures comprising a network of beam-like elements. We study the mechanics of discrete domes made of ribbons woven in a pentagonal triaxial pattern. Experiments and finite element simulations are performed to characteriz ...

ELSEVIER2023

DIET Controller: Dynamic Indoor Environment using Deep Reinforcement Learning

Arnab Chatterjee

Heating, Ventilation, and Air Conditioning (HVAC) Systems utilize much energy, accounting for 40% of total building energy use. The temperatures in buildings are commonly held within narrow limits, leading to higher energy use. Measurements from office bui ...

EPFL2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Emilio Maddalena

This thesis is situated at the crossroads between machine learning and control engineering. Our contributions are both theoretical, through proposing a new uncertainty quantification methodology in a kernelized context; and experimental, through investigat ...

EPFL2023

Self-sustained dynamics and forced resonant oscillations in flows: cross-junction jets and sloshing liquids

Alessandro Bongarzone

In engineering, oscillatory instabilities and resonances are often considered undesirable flow features and measures are taken to avoid them. This may include avoiding certain parametric regions or implementing control and mitigation strategies. However, t ...

EPFL2023

Reinforcement learning approach to control an inverted pendulum: A general framework for educational purposes

Jesus Sanchez Rodriguez

Machine learning is often cited as a new paradigm in control theory, but is also often viewed as empirical and less intuitive for students than classical model-based methods. This is particularly the case for reinforcement learning, an approach that does n ...

PUBLIC LIBRARY SCIENCE2023