Publications by Emilio Maddalena

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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 ...

This work proposes a differential evolution algorithm to control a vehicle-to-grid (V2G) system based on photovoltaic generation and energy cost curves, and constraints associated with the power converters' operation, battery charging strategy, and initial ...

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

We propose a non-parametric regression methodology that enforces the regressor to be fully consistent with the sample set and the ground-truth regularity assumptions. As opposed to the Nonlinear Set Membership technique, this constraint guarantees the atta ...

Model calibration for building systems is a key step to achieving accurate and reliable predictions that reflect the dynamics of real systems under study. Calibration becomes particularly challenging when integrating building and HVAC dynamics, due to larg ...

We present a methodology to learn explicit Model Predictive Control (eMPC) laws from sample data points with tunable complexity. The learning process is cast in a special Neural Network setting where the coefficients of two linear layers and a parametric q ...

Although it is known that having accurate Lipschitz estimates is essential for certain models to deliver good predictive performance, refining this constant in practice can be a difficult task especially when the input dimension is high. In this letter, we ...

A two-stage photovoltaic water-pumping system architecture is presented in this paper. In contrast with other alternatives available in the literature, the electronic drive does not exploit batteries to accomplish energy decoupling, neither large electroly ...

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 ...

This manuscript offers the perspective of experimentalists on a number of modern data-driven techniques: model predictive control relying on Gaussian processes, adaptive data-driven control based on behavioral theory, and deep reinforcement learning. These ...

In this technical article, we analyze the performance improvement and optimality properties of the learning model predictive control (LMPC) strategy for linear deterministic systems. The LMPC framework is a policy iteration scheme where closed-loop traject ...

Predictive control is a flexible control methodology that can optimize performance while satisfying current and voltage constraints. Its application in the power electronics domain is however hampered by the high computational demands associated with it. I ...

We propose Kernel Predictive Control (KPC), a learning-based predictive control strategy that enjoys deterministic guarantees of safety. Noise-corrupted samples of the unknown system dynamics are used to learn several models through the formalism of non-pa ...

Emilio Maddalena

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Machine Learning Methods for Robust Uncertainty Quantification and Controller Approximation

Differential Evolution and Fuzzy-Logic-Based Predictive Algorithm for V2G Charging Stations

Experimental data-driven model predictive control of a hospital HVAC system during regular use

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

Scalable Bayesian optimization for model calibration: Case study on coupled building and HVAC dynamics

A Neural Network Architecture to Learn Explicit MPC Controllers from Data

NSM Converges to a k-NN Regressor Under Loose Lipschitz Estimates

A Battery-Less Photovoltaic Water-Pumping System With Low Decoupling Capacitance

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

Lessons Learned from Data-Driven Building Control Experiments: Contrasting Gaussian Process-based MPC, Bilevel DeePC, and Deep Reinforcement Learning

On the Optimality and Convergence Properties of the Iterative Learning Model Predictive Controller

Robust Uncertainty Bounds in Reproducing Kernel Hilbert Spaces: A Convex Optimization Approach

Embedded PWM Predictive Control of DC-DC Power Converters Via Piecewise-Affine Neural Networks

KPC: Learning-Based Model Predictive Control with Deterministic Guarantees