Fixed-structure Control of LTI Systems with Polytopic-type Uncertainty : Application to Inverter-interfaced Microgrids

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Power Aware Placement of On-Chip Voltage Regulators

In traditional power delivery networks, the on-chip supply voltage is provided by board-level converters. Due to the significant distance between the converter and the load, variations in the load current are not effectively managed, producing a significan ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

Baiwei Guo

Control systems operating in real-world environments often face disturbances arising from measurement noise and model mismatch. These factors can significantly impact the perfor- mance and safety of the system. In this thesis, we aim to leverage data to de ...

EPFL2023

Robust optimization of control parameters for WEC arrays using stochastic methods

Tommaso Vanzan, Edie Miglio

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

Data-driven Methods for Control: from Linear to Lifting

Yingzhao Lian

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

Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Mario Paolone, Rahul Kumar Gupta

Increasing adoption of smart meters and phasor measurement units (PMUs) in power distribution networks are enabling the adoption of data-driven/model-less control schemes to mitigate grid issues such as over/under voltages and power-flow congestions. Howev ...

IEEE2023

Universal and adaptive methods for robust stochastic optimization

Ali Kavis

Within the context of contemporary machine learning problems, efficiency of optimization process depends on the properties of the model and the nature of the data available, which poses a significant problem as the complexity of either increases ad infinit ...

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

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

Homothetic Tube Model Predictive Control With Multi-Step Predictors

Giancarlo Ferrari Trecate, Danilo Saccani, Melanie Nicole Zeilinger

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

On Energy Control of Modular Multilevel Converters

Milan Utvic

Owing to the advancements in the area of power electronics, efficient and flexible ac to dc conversion is made possible, bringing back into focus the idea of the dc power transmission at various voltage levels. Several technical and economical factors advo ...

EPFL2023

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Experimental Validation of Model-less Robust Voltage Control using Measurement-based Estimated Voltage Sensitivity Coefficients

Mario Paolone, Rahul Kumar Gupta

IEEE2023