Distributed Control Research Papers - Academia.edu (original) (raw)

The IEC61499 is an open standard for distributed control and automation. The interface between control software and hardware or communications is achieved by means of the so-called Service Interface Function Blocks (SIFB). This paper presents the guidelines to build communication SIFBs based on the emerging OMG DDS (Data Distribution Service) middleware. This specification implements in a very efficient way the Publisher/Subscriber paradigm providing significant QoS configuration possibilities. These characteristics make DDS suitable for implementing the communications among time-critical devices. By using these DDS-SIFBs within IEC61499 code generation tools, the designers of the distributed applications will be allowed to use this powerful technology in the new distributed applications.

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- Middleware, Emerging Technologies, Distributed Control, Quality of Service

Critical Infrastructures, such as energy, banking, and transport, are an essential pillar to the well-being of the national and international economy, security and quality of life. These infrastructures are dependent on a spectrum of highly interconnected information infrastructures for their smooth, reliable and continuous operation. The field of protecting such Critical Information Infrastructures, or CIIP, faces numerous challenges, such as managing the secure interaction between peers, assuring the resilience and robustness of ...

- by Agus Tinus
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- Distributed Computing, Computer Networks, Packet Switching, Communication System
- by Rajesh Kumar
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- Computer Science, Image Processing, System Design, OPERATING SYSTEM
- by Lynn Stein
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- Computer Science, Relational Database, Web, Distributed Control
- by Frédéric Abergel
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- Applied Mathematics, Optimal Control, Fluid Mechanics, Distributed Control

L'avènement de l'industrie 4.0 introduit un ensemble de technologies d’information et de communication (TIC) qui permettent de distribuer le traitement de l’information et de décentraliser la prise de décision sur plusieurs entités autonomes et intelligentes tel que : les ressources de production, les opérateurs et les produits. Par conséquent les systèmes industriels deviennent de plus en plus complexes. Cette complexité pousse les industriels à concevoir et développer de nouvelles architectures de pilotage distribuées, plus flexibles et agiles faces aux perturbations. Pour y arriver les industriels sont contraints de passer par la phase de simulation qui leurs permet d'évaluer, tester et valider ces nouvelles architectures sans aucun risque. Dans la littérature en langue Française, on constate un manque de tutoriels pour apprendre à utiliser le logiciel de simulation FlexSim. L'objectif de cet article est de présenter un tel tutoriel pour introduire les principes de fonctionnement de FlexSim à travers une étude de cas de pilotage distribué où les produits jouent un rôle actif par leur capacité à prendre des décisions basées sur le mécanisme décisionnel Analytic Hierarchy Process (AHP).

- by ALI ATTAJER
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- Distributed Control, Flexsim, Tutorial, Analytical Hierarchy Process (AHP)
- by Gordon Bell
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- Distributed Computing, Computer Networks, Packet Switching, Communication System
- by Robert Babuska
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- Engineering, Game Theory, Reinforcement Learning, Multi Agent System

The focus of this paper is to develop a distributed control algorithm that will regulate the power output of multiple photovoltaic generators (PVs) in a distribution network. To this end, the cooperative control methodology from network control theory is used to make a group of PV generators converge and operate at certain (or the same) ratio of available power, which is determined by the status of the distribution network and the PV generators. The proposed control only requires asynchronous information intermittently from neighboring PV generators, making a communication network among the PV units both simple and necessary. The minimum requirement on communication topologies is also prescribed for the proposed control. It is shown that the proposed analysis and design methodology has the advantages that the corresponding communication networks are local, their topology can be time varying, and their bandwidth may be limited. These features enable PV generators to have both self-organizing and adaptive coordination properties even under adverse conditions. The proposed method is simulated using the IEEE standard 34-bus distribution network.

The design of distributed control systems (DCS) contains three major parts: (1) the programming of the control application code of the DCS, (2) the functional allocation of the control application code to the specific devices that the DCS consists of, and (3) the mapping of the distributed control application code to the underlying communication platform depending on the distribution. This

- by Athanasios P Kalogeras
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- Automatic Control, Control Systems, European Commission, Automation
- by Federica Mangiatordi
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- Information Security, Climate Change, Decision Making, Power Quality
- by Emanuele Carpanzano
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- Mechanical Engineering, Applied Mathematics, Complex Systems, Complex System
- by Rafael Ors
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- Digital Control, Neural Network, Intelligent Agent, Distributed Control
- by Abdul-Ganiyu Adisa Jimoh
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- Optimal Control, Distributed System, Comparative Study, Neural Network
- by Alanson Sample
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- System Design, Distributed Control, Mobile Robot, Sensor Array

In this paper, an online optimal distributed learning algorithm is proposed to solve leader-synchronization problem of nonlinear multi-agent differential graphical games. Each player approximates its optimal control policy using a single-network approximate dynamic programming (ADP) where only one critic neural network (NN) is employed instead of typical actorcritic structure composed of two NNs. The proposed distributed weight tuning laws for critic NNs guarantee stability in the sense of uniform ultimate boundedness (UUB) and convergence of control policies to the Nash equilibrium. In this paper, by introducing novel distributed local operators in weight tuning laws, there is no more requirement for initial stabilizing control policies. Furthermore, the overall closed-loop system stability is guaranteed by Lyapunov stability analysis. Finally, Simulation results show the effectiveness of the proposed algorithm.

- by Kleanthis Thramboulidis
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- Distributed Computing, Computer Networks, Scheduling, Distributed Control
- by Asitha Kulasekera
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- Distributed Control
- by James Nutaro
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- Information Technology, Control system, Distributed Control, Real Time Systems
- by Yves Durand
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- Channel Coding, Distributed Control, Real Time, Message Passing

This paper studies a time-delayed decentralized structural control strategy that aims to minimize the H2 norm of the closed-loop system. In a decentralized control system, control decisions are made based on data acquired from sensors located in the vicinity of a control device. Due to the non-convexity nature of the optimization problem caused by a decentralized architecture, controller design for decentralized systems remains a major challenge. In this work, a homotopy method is employed to gradually transform a centralized controller into multiple decentralized controllers. Linear matrix inequality (LMI) constraints are adopted in the homotopic transformation to ensure closed-loop control performance. In addition, multiple decentralized control architectures are implemented with a network of wireless sensing and control nodes. The sensor network allows simultaneous operation of multiple wireless subnets. Both the theoretical development and system implementation support the information overlapping between decentralized subnets. For validation, the wireless sensing and control system is installed on a six-story laboratory steel structure controlled by magnetorheological (MR) dampers. Shake-table experiments are conducted to demonstrate the performance of the wireless decentralized control strategies.

- by Sudhanshu Tripathi
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- Genetic Algorithm, Control system, Distributed Control, Intelligent System
- by Sameena Shah and +1
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- Ant Colony Optimization, Routing algorithm, Distributed Control, Command and Control
- by Ezequiel Di Paolo
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- Psychology, Cognitive Science, Philosophy, Social Evolution
- by Carlos Giron
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- Engineering, Power Electronics, FPGA, Power System
- by Alessandro Rizzo
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- Kinetics, Distributed Control, Long Range, Phase transition

Abstract Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It has proven to be effective for models with finite state and... more

- by Prashant Mehta
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- Nonlinear Control, Multi Agent System, Distributed Control, State Space

Cyber-physical energy systems require the integration of a heterogeneous physical layers and decision control networks, mediated by decentralized and distributed local sensing/actuation structures backed by an information layer. With the North American Electric Reliability Corporation (NERC) Critical Infrastructure Protection (CIP)[1] requirements and president's visions of more secure, reliable and controllable cyber-physical system, a new paradigm for modeling and research investigation is needed. In ...

- by raja r
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- Computer Vision, Remote Sensing, Control, Multi Agent System
- by IOSR Journals
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- Robotics, Distributed Control, Agile Manufacturing, Customization
- by Sophie Nallet
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- Carbon, Transcription Regulation, Transcription Factors, Glucose
- by M. Ferre
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- Information Flow, Distributed Control, Human Interaction, Telerobotics
- by Jungho Lee
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- System Integration, Mechanism Design, System Design, OPERATING SYSTEM

The concept of Value Net collaboration in which enterprises share values/risks and make business-critical decisions has attracted significant interest. However, lack of trust and support for cross-enterprise integration for collaborative decision-making has so far prevented this vision from becoming reality. This chapter examines the latter issue. Emerging technologies that support Value Net partners to connect their business processes and enable distributed decision making will be discussed. These technologies, and the trust they enable, fuse the boundaries between enterprises and between business and IT. They represent the critical first step to enable the Value Net to be adaptive and sense and respond to environmental changes.

- by Thierry H Van Cutsem
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- Engineering, Distributed Control, Induction Motor, Load Shedding
- by Dario Toncich
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- Engineering, Industrial Design, Flexible Manufacturing, Motor Control
- by Sape Mullender
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- Computational Complexity, Multidisciplinary, Computer Network, Distributed Control

Communication networks provide a larger flexibility with respect to the control design of large-scale interconnected systems by allowing the information exchange between the local controllers of the subsystems. The use of communication networks comes, however, at the price of non ideal signal transmission such as time delay which is a source of instability and deteriorates the control performance. This paper introduces an approach for the design of the communication topology for the distributed control of large-scale interconnected systems in order to optimize the whole system's performance in the presence of constant time delay. First, a decentralized control law that stabilizes the overall interconnected system is designed. Then the performance is improved by designing the distributed control law, i.e. allowing the controller of the subsystems to exchange information, by considering the time delay in the networks. As a novelty in this paper, the design of the communication topology between the controllers is also considered. The problem is formulated as a mixed-integer optimization problem. Furthermore, a method based on matrix perturbation theory is discussed to design the topology which also captures the relation between the time delay, controller gain and performance of the overall system. In addition, it is shown that the proposed strategy also guarantees the stability of the overall system under the permanent communication link failure. The results are validated through a numerical example.

- by Azwirman Gusrialdi
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- Integer Programming, Stability, Optimization Problem, Distributed Control