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Carlos Ocampo-Martinez

Researcher at Spanish National Research Council

Publications -  281
Citations -  4666

Carlos Ocampo-Martinez is an academic researcher from Spanish National Research Council. The author has contributed to research in topics: Model predictive control & Control theory. The author has an hindex of 30, co-authored 260 publications receiving 3430 citations. Previous affiliations of Carlos Ocampo-Martinez include Arizona State University & Polytechnic University of Catalonia.

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Advances in alkaline water electrolyzers: a review

TL;DR: In this article, the main focus is made on electrolysis, a mature procedure, and some developed proposals as previous steps to the hydrogen economy are presented, while some lines of research to improve alkaline electrolysis technology are commented.
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Application of predictive control strategies to the management of complex networks in the urban water cycle [Applications of Control]

TL;DR: The management of the urban water cycle is a subject of increasing interest because of its social, economic, and environmental impact as discussed by the authors, and the most important issues include sustainable use of limited resources and the reliability of service to consumers with adequate quality and pressure levels, as well as the urban drainage management to prevent flooding and polluting discharges to the environment.
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Modeling and real-time control of urban drainage systems: A review☆

TL;DR: In this article, the authors reviewed and discussed several techniques and strategies commonly used for the control of urban drainage systems and models to describe, simulate, and control the transport of wastewater in UDS.
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The Role of Population Games and Evolutionary Dynamics in Distributed Control Systems: The Advantages of Evolutionary Game Theory

TL;DR: This work addresses the main challenges for large-scale distributed systems, such as the amount of information needed to guarantee the proper operation of the system, the economic costs associated with the required communication structure, and the high computational burden of solving for the control inputs for largescale systems.
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Chance-Constrained Model Predictive Control for Drinking Water Networks

TL;DR: In this paper, a chance-constrained model predictive control (CC-MPC) strategy for the management of drinking water networks (DWNs) based on a finite horizon stochastic optimisation problem with joint probabilistic (chance) constraints is proposed.