55 citations · 181 across the 25 of their papers we have counts for
18 papers · 1 filter
Artificial-reference tracking MPC with probabilistically validated performance on industrial embedded systems
Victor Gracia, Pablo Krupa, Filiberto Fele +1
Industrial embedded systems are typically used to execute simple control algorithms due to their low computational resources. Despite these limitations, the implementation of advan…
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…
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…
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…
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…
Harmonic model predictive control for tracking sinusoidal references and its application to trajectory tracking
Pablo Krupa, Daniel Limon, Alberto Bemporad +1
Harmonic model predictive control (HMPC) is a recent model predictive control (MPC) formulation for tracking piece-wise constant references that includes a parameterized artificial…
Efficient online update of model predictive control in embedded systems using first-order methods
Victor Gracia, Pablo Krupa, Teodoro Alamo +1
Model Predictive Control (MPC) is typically characterized for being computationally demanding, as it requires solving optimization problems online; a particularly relevant point wh…
Tractable robust MPC design based on nominal predictions
Ignacio Alvarado, Pablo Krupa, Daniel Limon +1
Many popular approaches in the field of robust model predictive control (MPC) are based on nominal predictions. This paper presents a novel formulation of this class of controller…
Real-time implementation of MPC for tracking in embedded systems: Application to a two-wheeled inverted pendulum
Pablo Krupa, Jose Camara, Ignacio Alvarado +2
This article presents the real-time implementation of the model predictive control for tracking formulation to control a two-wheeled inverted pendulum robot. This formulation offer…
Chance constrained sets approximation: A probabilistic scaling approach -- EXTENDED VERSION
Martina Mammarella, Victor Mirasierra, Matthias Lorenzen +2
In this paper, a sample-based procedure for obtaining simple and computable approximations of chance-constrained sets is proposed. The procedure allows to control the complexity of…
Probabilistic interval predictor based on dissimilarity functions
A. Daniel Carnerero, Daniel R. Ramirez, Teodoro Alamo
This work presents a new methodology to obtain probabilistic interval predictions of a dynamical system. The proposed strategy uses stored past system measurements to estimate the…
Implementation of model predictive control for tracking in embedded systems using a sparse extended ADMM algorithm
Pablo Krupa, Ignacio Alvarado, Daniel Limon +1
This article presents a sparse, low-memory footprint optimization algorithm for the implementation of the model predictive control (MPC) for tracking formulation in embedded system…
Computationally efficient stochastic MPC: a probabilistic scaling approach
Martina Mammarella, Teodoro Alamo, Fabrizio Dabbene +1
In recent years, the increasing interest in Stochastic model predictive control (SMPC) schemes has highlighted the limitation arising from their inherent computational demand, whic…
Probabilistic reachable and invariant sets for linear systems with correlated disturbance
Mirko Fiacchini, Teodoro Alamo
In this paper a constructive method to determine and compute probabilistic reachable and invariant sets for linear discrete-time systems, excited by a stochastic disturbance, is pr…
Harmonic based model predictive control for set-point tracking
Pablo Krupa, Daniel Limon, Teodoro Alamo
This paper presents a novel model predictive control (MPC) formulation for set-point tracking. Stabilizing predictive controllers based on terminal ingredients may exhibit stabilit…
A probabilistic validation approach for penalty function design in Stochastic Model Predictive Control
Martina Mammarella, Teodoro Alamo, Sergio Lucia +1
In this paper, we consider a stochastic Model Predictive Control able to account for effects of additive stochastic disturbance with unbounded support, and requiring no restrictive…
Probabilistic performance validation of deep learning-based robust NMPC controllers
Benjamin Karg, Teodoro Alamo, Sergio Lucia
Solving nonlinear model predictive control problems in real time is still an important challenge despite of recent advances in computing hardware, optimization algorithms and tailo…
Randomized Methods for Design of Uncertain Systems: Sample Complexity and Sequential Algorithms
T. Alamo, R. Tempo, A. Luque +1
In this paper, we study randomized methods for feedback design of uncertain systems. The first contribution is to derive the sample complexity of various constrained control proble…