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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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6 papers · 1 filter

math.OC2026

Configuration-Constrained Tube MPC for Difference-of-Convex Nonlinear Systems

Filippo Badalamenti, Jose A. Borja-Conde, Filiberto Fele +2

This paper develops a convex tube model predictive control formulation for constrained nonlinear systems. We consider dynamics described by a discrete-time state-space model with p…

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…

math.OC2022★ 9 cited

Efficiently solving the harmonic model predictive control formulation

Pablo Krupa, Daniel Limon, Alberto Bemporad +1

Harmonic model predictive control (HMPC) is a model predictive control (MPC) formulation which displays several benefits over other MPC formulations, especially when using a small…

math.OC2021★ 12 cited

A sparse ADMM-based solver for linear MPC subject to terminal quadratic constraint

Pablo Krupa, Rim Jaouani, Daniel Limon +1

Model Predictive Control (MPC) typically includes a terminal constraint to guarantee stability of the closed-loop system under nominal conditions. In linear MPC this constraint is…

math.OC2021★ 5 cited

Restart of accelerated first order methods with linear convergence under a quadratic functional growth condition

Teodoro Alamo, Pablo Krupa, Daniel Limon

Accelerated first order methods, also called fast gradient methods, are popular optimization methods in the field of convex optimization. However, they are prone to suffer from osc…

math.OC2019

Restart FISTA with Global Linear Convergence

Teodoro Alamo, Pablo Krupa, Daniel Limon

Fast Iterative Shrinking-Threshold Algorithm (FISTA) is a popular fast gradient descent method (FGM) in the field of large scale convex optimization problems. However, it can exhib…