A Robust Data-Driven Controller Design Methodology With Applications to Particle Accelerator Power Converters

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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 ...

Ieee-Inst Electrical Electronics Engineers Inc2023

Robust optimization of control parameters for WEC arrays using stochastic methods

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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 ...

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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

Data-Driven Control and Optimization under Noisy and Uncertain Conditions

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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

Design and Development of a Diagnostic System for a Non-Intercepting Direct Measure of the SPIDER Ion Source Beamlet Current

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Stable and uniform beams with low divergence are required in particle accelerators; therefore, beyond the accelerated current, measuring the beam current spatial uniformity and stability over time is necessary to assess the beam performance, since these pa ...

MDPI2023

Training Efficient Controllers via Analytic Policy Gradient

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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 ...

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Learning and optimization of anticipatory feedback controllers for robot manipulation

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Programming intelligent robots requires robust controllers that can achieve desired tasks while adapting to the changes in the task and the environment. In this thesis, we address the challenges in designing such adaptive and anticipatory feedback controll ...

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Development of the self-modulation instability of a relativistic proton bunch in plasma

Ambrogio Fasoli, Sun Hee Kim, Ivo Furno, Patrick Blanchard, Ana Francisca Leal Silva Soares, Yanis Andrebe, Riccardo Agnello, Samuel Rey-Mermet, Christine Stollberg, Alban Sublet

Self-modulation is a beam-plasma instability that is useful to drive large-amplitude wakefields with bunches much longer than the plasma skin depth. We present experimental results showing that, when increasing the ratio between the initial transverse size ...

2023

Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

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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