•Control by clustering for maximizing the thermal power of solar fields.•Valves at the beginning of each loop increase the achieved thermal power.•The clustering criterion is to associate unbalanced ...loops dynamically.•Coalitional MPC approaches the optimal performance and can be carried in real-time.•Scalability and ease of deployment in large-scale CSP fields.
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.
This paper presents the design of a model predictive control (MPC) for the Calais canal, located in the north of France for satisfactory management of the system. To estimate the unknown ...inputs/outputs arising from the uncontrolled pumps, a digital twin (DT) in the framework of a Matlab-SIC2 is used to reproduce the dynamics of the canal, and the real database corresponding to a period of three days is employed to evaluate the control strategy. The canal is characterized by two operating modes due to high and low tides. As a consequence of this, time-varying constraints on the use of gates must be considered, which leads to the design of two multiobjective control problems, one for the high tide and another for the low tide. Furthermore, a moving horizon estimation (MHE) strategy is used to provide the MPC with unmeasured states. The simulation results show that the different objectives are met satisfactorily.
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.
•Clustering of controllers to maximize the energy produced by solar collector fields.•Loop controllers can demand and offer heat transfer fluid in a flow market.•Coalitions of loops are promoted dynamically to reach the control objective.•Improvements of thermal energy efficiency by up to 12%.•Feasible real-time implementation in large-scale fields due to its superior scalability.
•More flexible controller tuning is achieved combining DMPC and fuzzy approaches.•The control scheme provides scalability and ease of deployment in multi-agent systems.•Feasibility and stability are ...guaranteed for the proposed control method.•Various tuning parameters are considered to study the influence of fuzzy rules.
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 eight-coupled tank plant via simulation.
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.
A cooperative game theory framework is proposed to solve multi-robot task allocation (MRTA) problems. In particular, a cooperative game is built to assess the performance of sets of robots and tasks ...so that the Shapley value of the game can be used to compute their average marginal contribution. This fact allows us to partition the initial MRTA problem into a set of smaller and simpler MRTA subproblems, which are formed by ranking and clustering robots and tasks according to their Shapley value. A large-scale simulation case study illustrates the benefits of the proposed scheme, which is assessed using a genetic algorithm (GA) as a baseline method. The results show that the game theoretical approach outperforms GA both in performance and computation time for a range of problem instances.
•Cooperative game theory tools are considered to deal with MRTA problems.•Robots and tasks are defined and ranked in a game according to their Shapley value.•An algorithm is proposed to group the players into balanced clusters.•Randomized methods are applied to large problems to relieve the computational load.•The feasibility is assessed in a large scenario and contrasted with a genetic approach.
Clustering strategies are becoming increasingly relevant to boost the scalability of distributed control methods by focusing the cooperation efforts on highly coupled agents. They are also relevant ...in systems where failing communication links and plug-and-play events are considered, which demand increased flexibility and modularity. This article reviews commonalities and differences of those distributed strategies that exploit the degree of interaction between control agents to boost the mentioned properties, frequently leading to control structures where the communication network becomes a decision variable that may evolve dynamically. Taxonomies based on the control law employed, the criterion for selecting the network topology, its static/dynamical nature, the control architecture, and the provided theoretical properties, are given. Additionally, a review of applications in power networks, water systems, vehicle and traffic systems, renewable energy plants, and chemical processes is provided.
Climate change will intensify water scarcity, and therefore irrigation must be adapted to save water. Operational tools that provide watering recommendations to end-users are needed. This work ...presents a new tool, Irrigation-Advisor (IA), which is based on weather forecasts and is able to separately determine soil evaporation and crop transpiration, and thus is adaptable to a broad range of agricultural situations. By calculating several statistical indicators, IA was tested against the FAO-56 crop evapotranspiration (ETcFAO) methodology using local crop coefficients. Additionally, IA recommendations were compared with current standard practices by experienced farmers (F). Six field experiments with four widely cultivated species (endive, lettuce, muskmelon and potato) were performed in Southeast Spain. Irrigation water applied, crop yield, aboveground biomass and water productivity were determined. Crop water needs underestimations (5%–20%) were detected when comparing IA against ETcFAO, although the index of agreement proved reasonable adjustments. The IA recommendations led to water savings up to 13% when compared to F, except for lettuce, with a 31% surplus in irrigation when using IA. Crop yield was not compromised and water productivity was increased by IA. Therefore, IA mimicked the farmers′ irrigation strategies fairly well without deploying sensors on-site. Nevertheless, improvements are needed for increasing the accuracy of IA estimations.
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