R
Rudy R. Negenborn
Researcher at Delft University of Technology
Publications - 298
Citations - 7135
Rudy R. Negenborn is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Model predictive control & Computer science. The author has an hindex of 37, co-authored 270 publications receiving 5218 citations. Previous affiliations of Rudy R. Negenborn include Wuhan University of Technology.
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Design and control of hybrid power and propulsion systems for smart ships: A review of developments
TL;DR: In this paper, the authors classified ship propulsion topologies into combustion, electrochemical, stored and hybrid power supply, and analyzed which control strategies can improve performance of hybrid systems for future smart and autonomous ships and concluded that a combination of torque, angle of attack, and Model Predictive Control with dynamic settings could improve performance.
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Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities
TL;DR: This study provides a picture of what features have received more or less attention over the last years, bringing about potential research niches for new approaches in model-predictive control.
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Multi-agent model predictive control for transportation networks: Serial versus parallel schemes
TL;DR: This work proposes a novel serial coordination scheme based on Lagrange theory and compares this with an existing parallel scheme and shows that the serial scheme has preferable properties compared to the parallel scheme in terms of the convergence speed and the quality of the solution.
BookDOI
Distributed Model Predictive Control Made Easy
Jos M. Maestre,Rudy R. Negenborn +1 more
TL;DR: Distributed model predictive control (MPC) is one of the promising control methodologies for control of large-scale systems as discussed by the authors, and it has been widely used in the literature.
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Demand Response With Micro-CHP Systems
TL;DR: This paper investigates to what extent domestic energy costs could be reduced with intelligent, price-based control concepts (demand response) and proposes a model-predictive control strategy aimed at demand response for more intelligent control of micro-CHP systems.