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 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

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

Predictive Control of Cyber-Physical Systems

Predictive control encompasses a family of controllers that continually replan the system inputs during a certain time horizon to optimize their expected evolution according to a given criterion. This methodology has among its current challenges the adaptation to the paradigm of the so-called cyber-physical systems, which are composed of computers, sensors, actuators and physical entities of various kinds, including robots and even human beings who exchange information to control physical processes. This tutorial introduces the core concepts for the application of predictive control to cyber-physical systems by reviewing a series of examples that exploit the versatility of this design framework so as to solve the challenges presented by 21st century applications

Output feedback receding horizon control of constrained systems

Accepted versio

Optimal Control for Multi-mode Systems with Discrete Costs

This paper studies optimal time-bounded control in multi-mode systems with discrete costs. Multi-mode systems are an important subclass of linear hybrid systems, in which there are no guards on transitions and all invariants are global. Each state has a continuous cost attached to it, which is linear in the sojourn time, while a discrete cost is attached to each transition taken. We show that an optimal control for this model can be computed in NEXPTIME and approximated in PSPACE. We also show that the one-dimensional case is simpler: although the problem is NP-complete (and in LOGSPACE for an infinite time horizon), we develop an FPTAS for finding an approximate solution.Comment: extended version of a FORMATS 2017 pape

Analytical, Optimal, and Sparse Optimal Control of Traveling Wave Solutions to Reaction-Diffusion Systems

This work deals with the position control of selected patterns in reaction-diffusion systems. Exemplarily, the Schl\"{o}gl and FitzHugh-Nagumo model are discussed using three different approaches. First, an analytical solution is proposed. Second, the standard optimal control procedure is applied. The third approach extends standard optimal control to so-called sparse optimal control that results in very localized control signals and allows the analysis of second order optimality conditions.Comment: 22 pages, 3 figures, 2 table

Scaling properties of protein family phylogenies

One of the classical questions in evolutionary biology is how evolutionary processes are coupled at the gene and species level. With this motivation, we compare the topological properties (mainly the depth scaling, as a characterization of balance) of a large set of protein phylogenies with a set of species phylogenies. The comparative analysis shows that both sets of phylogenies share remarkably similar scaling behavior, suggesting the universality of branching rules and of the evolutionary processes that drive biological diversification from gene to species level. In order to explain such generality, we propose a simple model which allows us to estimate the proportion of evolvability/robustness needed to approximate the scaling behavior observed in the phylogenies, highlighting the relevance of the robustness of a biological system (species or protein) in the scaling properties of the phylogenetic trees. Thus, the rules that govern the incapability of a biological system to diversify are equally relevant both at the gene and at the species level.Comment: Replaced with final published versio

GPML: an XML-based standard for the interchange of genetic programming trees

We propose a Genetic Programming Markup Language (GPML), an XML based standard for the interchange of genetic programming trees, and outline the benefits such a format would bring in allowing the deployment of trained genetic programming (GP) models in applications as well as the subsidiary benefit of allowing GP researchers to directly share trained trees. We present a formal definition of this standard and describe details of an implementation. In addition, we present a case study where GPML is used to implement a model predictive controller for the control of a building heating plant

Prediction of Ideas Number During a Brainstorming Session

International audienceIn this paper, we present an approach allowing the prediction of ideas number during a brainstorming session. This prediction is based on two dynamic models of brainstorming, the non-cognitive and the cognitive models proposed by Brown and Paulus (Small Group Res 27(1):91–114, 1996). These models describe for each participant, the evolution of ideas number over time, and are formalized by differential equations. Through solution functions of these models, we propose to calculate the number of ideas of each participant on any time intervals and thus in the future (called prediction). To be able to compute solution functions, it is necessary to determine the parameters of these models. In our approach, we use optimization model for model parameters calculation in which solution functions are approximated by numerical methods. We developed two generic optimization models, one based on Euler’s and the other on the fourth order Runge–Kutta’s numerical methods for the solving of differential equations, and we apply them to the non-cognitive and respectively to the cognitive models. Through some feasibility tests, we show the adequacy of the proposed approach to our prediction context