Teodoro Alamo

This work approximates offline the feedback law of an NMPC using universal kriging, avoiding online optimization. From state-action pairs, a standard UK policy and an ℓ1-regularized variant on the kriging weights are built to promote sparser and more interpretable interpolations. We demonstrate the effectiveness of the methodology using a simulated swing-up model of a torque-constrained inverted pendulum.

Industrial embedded systems are typically used to execute simple control algorithms due to their low computational resources. Despite these limitations, the implementation of advanced control techniques such as Model Predictive Control (MPC) has been explored by the control community in recent years, typically considering simple linear formulations or explicit ones to facilitate the online computation of the control input. These simplifications often lack features and properties that are desirable in real-world environments. In this article, we present an efficient implementation for embedded systems of MPC for tracking with artificial reference, solved via a recently developed structure-exploiting first-order method. This formulation is tailored to a wide range of applications by incorporating essential practical features at a small computational cost, including integration with an offset-free scheme, back-off parameters that enable constraint tightening, and soft constraints that preserve feasibility under disturbances or plant-model mismatch. We accompany this with a framework for probabilistic performance validation of the closed-loop system over long-term operation. We illustrate the applicability of the approach on a Programmable Logic Controller (PLC), incorporated in a hardware-in-the-loop setup to control a nonlinear continuous stirred-tank reactor. The behavior of the closed-loop system is probabilistically validated with respect to constraint violations and the number of iterations required at each time step by the MPC optimization algorithm.

En este trabajo abordamos el problema de predicción en tiempo real de frecuencia en la red de distribución. Para esto, proponemos una formulación basada en datos utilizando el método kriging ordinario. Presetamos un oráculo de predicción no paramétrico que permite estimar la trayectoria futura de la frecuencia de red directamente a partir de datos históricos de intensidad y frecuencia. Demostramos la efectividad de nuestra metodología mediante un modelo simulado de un nodo de la red de distribución.

The supervised classification recognizes patterns in the data to separate classes of behaviors. Canonical solutions contain misclassification errors that are intrinsic to the numerical approximating nature of machine learning (ML). The data analyst may minimize the classification error on a class at the expense of increasing the error of the other classes. The error control of such a design phase is often done in a heuristic manner. In this article, it is key to develop theoretical foundations capable of providing probabilistic certifications to the obtained classifiers. In this perspective, we introduce the concept of probabilistic safety region to describe a subset of the input space in which the number of misclassified instances is probabilistically controlled. The notion of adjustable classifiers, a special class of classifiers that share the property of being controllable by a scalar parameter, is then exploited to link the tuning of ML with error control. Several tests and examples corroborate the approach. They are provided through the synthetic data in order to highlight all the steps involved, as well as notable benchmark datasets and a smart mobility application.

Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial harmonic reference as a decision variable, resulting in an increased performance and domain of attraction with respect to other MPC formulations. This article presents an extension of the HMPC formulation to track periodic harmonic/sinusoidal references and discusses its use for tracking arbitrary trajectories. The proposed formulation inherits the benefits of its predecessor, namely its good performance and large domain of attraction when using small prediction horizons, and that the complexity of its optimization problem does not depend on the period of the reference. We show closed-loop results discussing its performance and comparing it to other MPC formulations.