16 citations · 73 across the 21 of their papers we have counts for
15 papers · 1 filter
Learning practically stabilizing output-feedback nonlinear controllers
Kui Xie, Pablo Krupa, Alberto Bemporad
This paper addresses the problem of learning an output-feedback surrogate controller offline that approximates a given, possibly computationally expensive, nonlinear controller-obs…
Active Learning MPC Objective Functions from Preferences
Hasna El Hasnaouy, Pablo Krupa, Mario Zanon +1
Designing the objective function in Model Predictive Control (MPC) is challenging when performance assessment criteria are available only from human judgment. We adopt a preference…
Learning the MPC objective function from human preferences
Pablo Krupa, Hasna El Hasnaouy, Mario Zanon +1
In Model Predictive Control (MPC), the objective function plays a central role in determining the closed-loop behavior of the system, and must therefore be designed to achieve the…
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…
Learning disturbance models for offset-free reference tracking
Pablo Krupa, Mario Zanon, Alberto Bemporad
This work presents a nonlinear control framework that guarantees asymptotic offset-free tracking of generic reference trajectories by learning a nonlinear disturbance model, which…
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…
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…
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…