activity
20132026
most citedProbabilistic performance validation of deep learning-based robust NMPC controllers

55 citations · 181 across the 25 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

eess.SY2024★ 1 cited

Recent advancements on MPC for tracking: periodic and harmonic formulations

Pablo Krupa, Daniel Limon, Teodoro Alamo

The main benefit of model predictive control (MPC) is its ability to steer the system to a given reference without violating the constraints while minimizing some objective. Furthe…

eess.SY2024

Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Pablo Krupa, Johannes Köhler, Antonio Ferramosca +4

This paper provides a comprehensive tutorial on a family of Model Predictive Control (MPC) formulations, known as MPC for tracking, which are characterized by including an artifici…

eess.SY2024★ 4 cited

Implementation of soft-constrained MPC for Tracking using its semi-banded problem structure

Victor Gracia, Pablo Krupa, Daniel Limon +1

Model Predictive Control (MPC) is a popular control approach due to its ability to consider constraints, including input and state restrictions, while minimizing a cost function. H…

math.OC2024

Model Predictive Control for setpoint tracking

Daniel Limon, Antonio Ferramosca, Ignacio Alvarado +1

The main objective of tracking control is to steer the tracking error, that is the difference between the reference and the output, to zero while the plant's operation limits are s…

eess.SY2024★ 4 cited

Efficient implementation of MPC for tracking using ADMM by decoupling its semi-banded structure

Victor Gracia, Pablo Krupa, Daniel Limon +1

Model Predictive Control (MPC) for tracking formulation presents numerous advantages compared to standard MPC, such as a larger domain of attraction and recursive feasibility even…