Market-based clustering of model predictive controllers for maximizing collected energy by parabolic-trough solar collector fields

This article focuses on maximizing the thermal energy collected by parabolic-trough solar collector fields to increase the production of the plant. To this end, we propose a market-based clustering model predictive control strategy in which controllers of collector loops may offer and demand heat transfer fluid in a market. When a transaction is made between loop controllers, a coalition is formed, and the corresponding agents act as a single entity. The proposed hierarchical algorithm fosters the formation of coalitions dynamically to improve the overall control objective, increasing the thermal energy delivered by the field. Finally, the proposed controller is assessed via simulation with other control methods in two solar parabolic-trough fields. The results show that the energy efficiency with the clustering strategy outperforms by 12% that of traditional controllers, and the method is implementable in real-time to control large-scale solar collector fields, where significant gains in thermal collected energy can be obtained, due to its scalability

A Coalitional Model Predictive Control for the Energy Efficiency of Next-Generation Cellular Networks

Next-generation cellular networks are large-scale systems composed of numerous base stations interacting with many diverse users. One of the main challenges with these networks is their high energy consumption due to the expected number of connected devices. We handle this issue with a coalitional Model Predictive Control (MPC) technique for the case of next-generation cellular networks powered by renewable energy sources. The proposed coalitional MPC approach is applied to two simulated scenarios and compared with other control methods: the traditional best-signal level mechanism, a heuristic algorithm, and decentralized and centralized MPC schemes. The success of the coalitional strategy is considered from an energy efficiency perspective, which means reducing on-grid consumption and improving network performance (e.g., number of users served and transmission rates)

Actualización y control basado en datos de un proceso de cuatro tanques interconectados

El control de procesos es una parte esencial para obtener resultados de unas determinadas características de forma que se cumpla con unas especificaciones y unos niveles de calidad exigidos. En las últimas décadas, la necesidad de crear procesos más eficientes que se adapten a los cambios de tecnologías y a las preocupaciones medioambientales ha generado cambios en las técnicas de control y se han desarrollado, considerablemente, los métodos de control predictivo. El control predictivo basado en modelo (MPC) es una estrategia de control avanzado que se basa en el uso de un modelo dinámico del sistema para predecir el comportamiento futuro de dicho sistema y, en base a este comportamiento futuro, predecir la señal de control futura que minimice una función objetivo teniendo en cuenta unas restricciones.Universidad de Sevilla. Máster Universitario en Ingeniería Electrónica, Robótica y Automátic

Industrial IoT devices and cyber-physical production systems: review and use case

“This is a post-peer-review, pre-copyedit version of an article published in Lecture Notes in Electrical Engineering. The final authenticated version is available online at: https://doi.org/10.1007/978-3-319-91334-6_40"The present paper describes the state of the art related to IIoT Devices and Cyber-Physical systems and presents a use case related to predictive maintenance. Industry 4.0 is the boost for smart manufacturing and demands flexibility and adaptability of all devices/machines in the shop floor. The machines must become smart and interact with other machines inside and outside the industries/factories. The predictive maintenance is a key topic in this industrial revolution. The reason is based on the idea that smart machines must be capable to automatically identify and predict possible faults and actuate before they occur. Vibrations can be problematic in electrical motors. For this reason, we address an experimental study associated with an automatic classification procedure, that runs in the smart devices to detect anomalies. The results corroborate the applicability and usefulness of this machine learning algorithm to predict vibration faults.info:eu-repo/semantics/acceptedVersio

A light clustering model predictive control approach to maximize thermal power in solar parabolic-trough plants

This article shows how coalitional model predictive control (MPC) can be used to maximize thermal power of large-scale solar parabolic-trough plants. This strategy dynamically generates clusters of loops of collectors according to a given criterion, thus dividing the plant into loosely coupled subsystems that are locally controlled by their corresponding loop valves to gain performance and speed up the computation of control inputs. The proposed strategy is assessed with decentralized and centralized MPC in two simulated solar parabolic-trough fields. Finally, results regarding scalability are also given using these case studies

A Coalitional Model Predictive Control Approach for Heterogeneous Cellular Networks

Heterogeneous cellular networks (HetNets) are large-scale systems that comprise numerous base stations interacting with a significant number of users of diverse types. Finding a trade-off between energy consumption and quality of service is one of the major challenges in these networks. To deal with this issue, a coalitional model predictive control (MPC) approach is proposed for a HetNet powered by renewable power sources, and compared in simulation with the traditional best-signal level mechanism and the centralized MPC method. Furthermore, other key performance indicators associated with grid consumption such as the number of served users and transmission rates are also evaluated

Robust coalitional model predictive control with plug-and-play capabilities

This article presents a distributed implementation of a model predictive controller with information exchange to manage a distributed networked system of coupled dynamic subsystems. We propose a coalitional control method, where local controllers coalesce into clusters to improve performance, as a tool to solve plug-and-play problems. Our main contribution is a tube-based coalitional approach that employs online optimized invariant sets. These sets are instrumental in guaranteeing recursive feasibility and stability when faced with plug-and-play operations, i.e., subsystems joining or leaving the network. We also explore the inherent robustness properties to absorb disturbances not covered by the tubes without the need to group local controllers. Finally, the simulation results show the benefits of our proposed control method.(c) 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

Robust Coalitional Model Predictive Control With Predicted Topology Transitions

This article presents a novel clustering model predictive control technique where transitions to the best cooperation topology are planned over the prediction horizon. A new variable, the so-called transition horizon, is added to the optimization problem to calculate the optimal instant to introduce the next topology. Accordingly, agents can predict topology transitions to adapt their trajectories while optimizing their goals. Moreover, conditions to guarantee recursive feasibility and robust stability of the system are provided. Finally, the proposed control method is tested via a simulated eight-coupled tanks plant

Hierarchical distributed model predictive control based on fuzzy negotiation

This work presents a hierarchical distributed model predictive control approach for multiple agents with cooperative negotiations based on fuzzy inference. Specifically, a fuzzy-based two-layer control architecture is proposed. In the lower control layer, there are pairwise negotiations between agents according to the couplings and the communication network. The resulting pairwise control sequences are sent to a coordinator in the upper control layer, which merges them to compute the final ones. Furthermore, conditions to guarantee feasibility and stability in the closed-loop system are provided. The proposed control algorithm has been tested on an eightcoupled tank plant via simulation

On Data Reutilization for Historian Based Predictive Control

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study