R
Raffaele Soloperto
Researcher at University of Stuttgart
Publications - 21
Citations - 462
Raffaele Soloperto is an academic researcher from University of Stuttgart. The author has contributed to research in topics: Model predictive control & Constraint satisfaction. The author has an hindex of 9, co-authored 17 publications receiving 229 citations. Previous affiliations of Raffaele Soloperto include École Polytechnique Fédérale de Lausanne & University of Bologna.
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Journal ArticleDOI
A Computationally Efficient Robust Model Predictive Control Framework for Uncertain Nonlinear Systems
TL;DR: In this article, the shape of the tube is based on an offline computed incremental Lyapunov function with a corresponding (nonlinear) incrementally stabilizing feedback, and the online optimization only implicitly includes these nonlinear functions in terms of scalar bounds.
Journal ArticleDOI
A passivity-based approach to voltage stabilization in DC microgrids with ZIP loads
TL;DR: To prove voltage stability in the closed-loop microgrid, the LaSalle’s invariance theorem is exploited, and explicit inequalities on control gains to design local controllers are provided, allowing removal and addition of DGUs in a plug-n-play fashion.
Journal ArticleDOI
Learning-Based Robust Model Predictive Control with State-Dependent Uncertainty
TL;DR: By explicitly considering the state dependency of the uncertainty sets in the RMPC approach, it is shown how closed-loop performance can be improved over existing approaches that consider worst-case uncertainty.
Proceedings ArticleDOI
Collision avoidance for uncertain nonlinear systems with moving obstacles using robust Model Predictive Control
TL;DR: A novel robust collision avoidance approach that is based on a general tube-based MPC framework that provides formal guarantees, such as recursive feasibility, constraint satisfaction, as well as robust collisionavoidance, is provided.
Proceedings ArticleDOI
Linear robust adaptive model predictive control: Computational complexity and conservatism
TL;DR: This paper presents a robust adaptive model predictive control scheme for linear systems subject to parametric uncertainty and additive disturbances that provides a computationally efficient formulation with theoretical guarantees (constraint satisfaction and stability), while allowing for reduced conservatism and improved performance due to online parameter adaptation.