9 papers
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…
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…
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 generalized Nash equilibria from pairwise preferences
Pablo Krupa, Alberto Bemporad
Generalized Nash Equilibrium Problems (GNEPs) arise in many applications, including non-cooperative multi-agent control problems. Although many methods exist for finding generalize…
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…
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…