1,720,965 research outputs found
Parsimonious Cooperative Distributed MPC for Tracking Piece-Wise Constant Setpoints
Get PDFDistributed Model Predictive Control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of inputs to regulate a subset of outputs composing the overall system. These controllers may cooperate to find an optimal control sequence that minimizes a global cost function, as in the case of Cooperative Distributed Model Predictive Control (CD-MPC). In this paper two linear CD-MPC algorithms for tracking are proposed. The aim of these controllers is to drive the outputs of the overall system to any admissible piece-wise constant set-point, satisfying input and state constraints. However, in the available literature this result is achieved by using a set of centralized variables that keep track of the global state of the system. In contrast, we develop novel CD-MPC approaches for tracking that rely on “as local as possible” information instead of the plant-wide information flow. These new control strategies reduce the required communication overhead, local computational demands, and are more scalable than CD-MPC algorithms available in the literature. We illustrate the main characteristics and benefits of the proposed approaches by means of a multiple evaporator process example
Parsimonious cooperative distributed MPC algorithms for offset-free tracking
Get PDFWe propose in this paper novel cooperative distributed MPC algorithms for tracking of piecewise constant setpoints in linear discrete-time systems. The available literature for cooperative tracking requires that each local controller uses the centralized state dynamics while optimizing over its local input sequence. Furthermore, each local controller must consider a centralized target model. The proposed algorithms instead use a suitably augmented local system, which in general has lower dimension compared to the centralized system. The same parsimonious parameterization is exploited to define a target model in which only a subset of the overall steady-state input is the decision variable. Consequently the optimization problems to be solved by each local controller are made simpler. We also present a distributed offset-free MPC algorithm for tracking in the presence of modeling errors and disturbances, and we illustrate the main features and advantages of the proposed methods by means of a multiple evaporator process case study
Distributed model predictive control for energy management in a network of microgrids using the dual decomposition method
Get PDFThis paper deals with the application of model predictive control (MPC) to optimize power flows in a network of interconnected microgrids (MGs). More specifically, a distributed MPC (DMPC) approach is used to compute for each MG how much active power should be exchanged with other MGs and with the outer power grid. Due to the presence of coupled variables, the DMPC approach must be used in a suitable way to guarantee the feasibility of the consensus procedure among the MGs. For this purpose, we adopt a tailored dual decomposition method that allows us to reach a feasible solution while guaranteeing the privacy of single MGs (ie, without having to share private information like the amount of generated energy or locally consumed energy). Simulation results demonstrate the features of the proposed cooperative control strategy and the obtained benefits with respect to other classical centralized control methods
Human-Machine Interface for Multi-Agent Systems Management using the Descriptor Function Framework
No full textHuman-machine interfaces for command and control of teams of autonomous agents is an enabling technology for the development of reliable multi-agent systems. Tools for proper modelling of these systems are sought in order to ease the creation of efficient interface that allow a single operator to control several agents, as well as monitor the execution state of the tasks the team is demanded to accomplish. If humans are present in the environment, the agents must sense their presence and
collaborate with them toward the mission accomplishment. In this context, the descriptor function framework is a versatile tool that allows the human integration at two levels: the development of human-machine interfaces and the achievement of human-machine teaming. In this paper, we show how such results can be obtained and we propose a possible architecture for the framework implementation
ISME activity on the use of Autonomous Surface and Underwater Vehicles for acoustic surveys at sea
Get PDFThe paper presents an overview of the recent and ongoing research activities of the Italian Interuniversity Center on Integrated Systems for the Marine Environment (ISME) in the field of geotechnical seismic surveying. Such activities, performed in the framework of the H2020 European project WiMUST, include the development of technologies and algorithms for Autonomous Surface Crafts and Autonomous Underwater Vehicles to perform geotechnical seismic surveying by means of a team of robots towing streamers equipped with acoustic sensors
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Advances on Distributed Model Predictive Control
No full textModel Predictive Control is a class of advanced control techniques, widely used especially in the process industries, and it has its fundamentals in optimal control. Further, several class of predictive controllers were developed in the last two decades.
Nevertheless, conventional feedback controllers (e.g., PID) are, so far, the de-facto standard for most industrial applications. This is due to the fact that no system model is necessary for these approaches.
On the other hand, in case of large-scale applications, conventional feedback controllers are no longer appropriate since industrial control systems are often decentralized (i.e., interactions among subsystems are not considered). Due to dynamic coupling it is well known that performance may be poor, and stability properties may be even lost.
MPC provides several form of distributed approaches that guarantee nominal closed-loop stability and convergence to the centralized optimal performance.
This thesis shows recent research activities on Distributed MPC to demonstrate how is getting a mature technology, suitable to be applied to different application areas and large-scale systems, with computational as well as organizational advantages.
This could lead to use MPC beyond its control aspects, in order to exploit its management capabilities.
This work shows how Distributed Model Predictive Control, not only seems to fit the new technologies that are entering the global market, by creating a new interesting opportunities, but also could become a keyword for the emerging "smart factory"
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Cooperative Tracking using Model Predictive Control
No full textDistributed Model Predictive Control refers to a class of predictive control architectures in which a number of local controllers manipulate a subset of input and output composing the overall system. These controllers may cooperate to find an optimal control sequence that minimize a global cost function, as in the case of Cooperative Distributed Model Predictive Control (CD-MPC).
In this thesis several types of linear CD-MPC controller for tracking are studied. The aim of these controllers is to drive the overall system to an admissible set-point, satisfying hard input and state constraints. However, this result, in literature, is achieved by using a set of centralized variables that keep track of the global state of the system.
In this context, I developed a novel CD-MPC approach for tracking that relies on local information instead of the plant-wide information flow. This new control strategy reduces communication overhead and is more scalable than classical CD-MPC presented in literature.
Il controllo predittivo distribuito si riferisce ad una classe specifica di controllo predittivo in cui i controllori calcolano localmente gli ingressi sfruttando solo un sottoinsieme delle variabili del sistema globale. Tali controllori possono cooperare per trovare una sequenza ottima di controlli che minimizzano una funzione obiettivo globale, come nel caso del Cooperative Distributed Model Predictive Control (CD-MPC).
In questa tesi sono implementati più tipi di controllori lineari CD-MPC per il tracking. Lo scopo di tali controllori è di far convergere il sistema globale su un set-point ammissibile, soddisfacendo eventuali vincoli di stato e ingresso. Comunque, in letteratura, tale risultato sul tracking è raggiunto usando un insieme di variabili centralizzate informative del sistema globale.
In questo contesto è stato quindi proposto un nuovo approccio a CD-MPC per il tracking che si basa su informazioni locali piuttosto che su tutto il flusso di informazioni del sistema nel suo insieme. Questa nuova strategia di controllo permette di ridurre il livello di congestione di rete ed è più scalabile degli algoritmi presenti attualmente in letteratura per questo tipo di problema
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