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Y Stability and performance verification of optimization-based controllers

Publications associées (56)

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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 ...
2023

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

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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 ...
Piscataway2023

First Order Methods For Globally Optimal Distributed Controllers Beyond Quadratic Invariance

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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 methods for building control - A review and promising future directions

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A review of the heating, ventilation and air-conditioning control problem for buildings is presented with particular emphasis on its distinguishing features. Next, we not only examine how data-driven algorithms have been exploited to tackle the main challe ...
PERGAMON-ELSEVIER SCIENCE LTD2020

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

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We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while ...
2019

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

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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

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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

SVR-AMA: An Asynchronous Alternating Minimization Algorithm With Variance Reduction for Model Predictive Control Applications

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This paper focuses on the design of an asynchronous dual solver suitable for model predictive control (MPC) applications. The proposed solver relies on a state-of-the-art variance reduction (VR) scheme, previously used in the context of proximal stochastic ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Coordinated Optimization and Control for Smart Grids

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In this thesis, we consider commercial buildings with available heating, ventilation and air conditioning (HVAC) systems, and develop methods to assess and exploit their energy storage and production potential to collectively offer ancillary services to th ...
EPFL2018

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