Intelligent Distributed Control Research Articles

The role of Wireless Sensor Networks in the area of Critical Information Infrastructure Protection

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 the overall system, and deploying warning and alert systems, amongst others. In this tapestry of CIIP, Wireless Sensor Networks can be used as an invaluable tool due to their intelligent distributed control capabilities, alongside with their capability to work under severe conditions. In this paper, we justify why Wireless Sensor Networks technology is suitable for providing security for these scenarios, describing both their advantages and research issues and their role in the overall scheme of protecting the Critical Information Infrastructures.

Design of intelligent distributed control systems: a dependability point of view

The use of fieldbuses and the emergence of intelligent sensors and actuators are opening up new possibilities for distributed control systems, but are also introducing additional constraints in terms of achieving dependability objectives. The type of production environment will greatly determine the predominant criterion for an automatic control system, i.e. reliability, availability, maintainability, safety, etc. On the other hand, the choice of a fieldbus system will also depend on factors such as application size, data throughput, and integration of time considerations. Other important criteria include cost, confidentiality, and compatibility with existing equipment. Therefore, it appears essential that designers be given the means to assess dependability at each design step by integrating feedback from experience. Assessing dependability is too often limited to an evaluation at the end of the design process, which often involves reselecting previous choices. The main topic of this paper is to focus on the communication function which is a pivotal of intelligent distributed control systems. So this article is a synthesis of different aspects linked to the design of fieldbus based applications thanks to the contributors, who come from various fields. Consequently it highlights the main problem and give some ways to solve them.

Setting a standard for building control

The protectionist attitudes of manufacturers have hampered the development of intelligent buildings. Now a consensus is emerging that benefits both suppliers and customers. Among the competing systems, a clear winner is emerging. According to Echelon, which developed the protocol, LonWorks now accounts for 67% of the worldwide market for intelligent commercial buildings. More and more manufacturers and customers are investing in this technology, which is designed to be truly open and interoperable and is winning global acceptance. The philosophy underlying LonWorks is intelligent distributed control. Every node on the network has its own address and contains embedded intelligence to control the operation of attached sensors or actuators and implement the communications protocol.

Intelligent distributed simulation and control of power plants

This paper presents summaries of five research and development activities in intelligent distributed simulation and control of power plants which were presented in a panel session of the same name at the IEEE Power Engineering Society Winter Meeting on February 6, 1997 in New York City. Each of the panelists discussed methods of how they have incorporated intelligent systems techniques into their research and development efforts in power plant control. The panel was organized by the Working group on intelligent methods in station control, station control subcommittee, and the energy development and power generation committee of the IEEE power engineering society.

Intelligent semi-autonomous vehicles in materials handling

An increase in functionality of semi-autonomous vehicles (SAV) through the implementation of intelligent distributed control and smart sensing techniques is presented. In combination with a modular design approach, this facilitates system modification and improvement, combined with faster customisation of the platform. A distributed and reactive behavioural control architecture is used to realise local autonomous navigation capabilities; improved operator interaction; self protection and safer operation. A virtual engineering environment based on a computer-aided-graphics platform is used (1) for modelling the vehicle and the environment in which it can operate; (2) developing pre-emptive learning and training of responses/behaviours, and (3) for evaluation of vehicle functionality as part of an integrated materials handling system.

Combined research and curriculum development for power plant intelligent distributed control

An NSF project, combined research and curriculum development for power plant intelligent distributed control, was conducted from 1992 to 1996, and expanded to a power system intelligent coordinated control project. Two new graduate courses were developed for power plant control. The capstone power plant intelligent distributed control course covers: (1) real-time large scale power plant simulations using UNIX network programming; (2) distributed implementation of advanced controls in an architecture of workstations and microprocessor-based controllers; and (3) intelligent controls using fuzzy logic, neural networks and genetic algorithms. Along with the curriculum development update, a summary of significant research activities is presented in order to provide a complete overview of the results of the projects.

Strategies for simultaneous multiple autonomous underwater vehicle operation and control

Under sampling of the coastal oceans remains a persistent problem for standard oceano-graphic measurement practice wherein an instrument package is tethered to a research vessel. The overhead costs associated with operating a large research vessel impose a strict minimum on the cost of data collected. Owing to the overheads, significant improvements in sampling technology on the tethered platform can only produce modest gains in the cost effectiveness. In contrast, untethered vehicles if operated simultaneously have the potential to increase cost effectiveness significantly by distributing the overhead costs over several sampling platforms. Furthermore, synoptic and pseudosynoptic data can be collected with multiple autonomous underwater vehicles (AUVs), thereby providing the type of information critical to dynamic process modeling unattainable with non-synoptic data. While the goal of simultaneous multiple-vehicle operation has been espoused over the last few years, AUV technology and practice have until recently been too immature to realize that potential. Recently, Florida Atlantic University (FAU) has developed a new series of modular AUVs with the express purpose of supporting multiple sensors and multiple-vehicle operation. This series of vehicle is called the Ocean Explorer of which three have been produced so far. This paper will explore some of the associated navigation, tracking, control and deployment problems associated with multiple-vehicle operation in coastal applications. In addition, the characteristics of the component level intelligent distributed control system, integrated data logger and vehicle control system will be discussed. In particular this paper will discuss how FAU has applied the concepts of elastic constraint propagation and the symmetric fuzzy decision-making model to AUV control systems. Some results of early experiments in synoptic data collection with a conductivity, temperature and depth (CTD) sensor using multiple AUVs for the determination of horizontal structure will be described.

宇宙ロボット 分散知能型制御構造による歩行ロボットの歩容生成

This paper proposes a distributed intelligent controller for multi-legged walking robots. Since living things have nerve center to control basic exercise motion, walking robots should also have such distributed low level controllers to govern the fundamental local motion. The proposed controller tries to simulate the architecture of the nerve center; its control structure consists of multiple controllers, where each controller corresponds to each leg and it determines the motion of its leg by common information and its own local information. The central controller needs not to consider the detail of each leg motion; it only does the global path planning and adjust the total synchronization. Distributed intelligent Control Structure is like a intelligence cluster and consists of Intelligent Clock Generator (ICG), Distributed Intelligent Controller (DIC) and Common Data Field (CDF) . Distributed intelligent control structure generates some gaits naturally. It is also easy to add other function to this system. This system does not depend on number or structure of robot's leg. This paper describes the details of each functions of controllers, and explains that the whole system works well only by joing these functions. Several numerical simulations verify the validty of the proposed control structure.

Knowledge Analysis of Tasks as a Basis for Interface Design of Complex Developmental Systems

Complex development systems are interactive software systems used for the manipulation, design or development in complex problem domains. This paper highlights some of the limitations of Johnson's Knowledge Analysis of Tasks (KAT) (Johnson, 1989, 1992) and proposes a modified version of KAT where task knowledge can be analysed and grouped in a way that will make it useful in the derivation of interfaces in complex developmental systems. This modified form has been applied to the domain of intelligent distributed control systems in an attempt to develop interface concepts for the development, installation and documentation of such systems. The paper further shows how this extended version of KAT may prove a useful input to object oriented analysis.

Multilevel intelligent fuzzy control of oversaturated urban traffic networks

Some drawbacks of existing automated urban traffic control systems are discussed. It is shown that most of them are based on deterministic algorithms and have a centralized structure. As a result, qualitative and quantitative complexity of transport processes are not taken into account effectively. To overcome this inconvenience, an approach for implementing distributed intelligence systems is proposed. It is based on multilevel and artificial intelligence techniques. Fuzzy sets theory is chosen as a tool for distributed intelligence control systems design and a specific method is presented. It is shown, however, that this method is not suitable for real time control of large scale systems and an alternative method, leading to a closed-loop fuzzy control law, is proposed. It is based on decomposition of the system into subsystems and presentation as a hierarchical two-level control structure. In this case, the controls have two components—local (calculated by the lower level) and global (calculated by the...

Intelligent operation of distribution network

The authors present an intelligent distribution control system operating in conjunction with a SCADA system with minimum supervision and interaction with a human operator. The application of a model-based expert system results in an intelligent power distribution system with learning, self organising and diagnostic capabilities. The work deviates from previous approaches in two important aspects: the use of model-based reasoning allows many different application knowledge bases (e.g. fault diagnosis, restoration, maintenance planning, etc.) to operate on the common model of the distribution network thus making it very compact and versatile, and it introduces for the first time the concept of intelligence in a power distribution system. To support modular development and simple maintenance of the expert system, the knowledge required to do the reasoning has been separated into a model of the distribution system and several knowledge bases which work on it. The frame-based object-oriented knowledge representation provides the flexibility to extend the model without adversely affecting the performance. The expert system has been tested extensively for operation with a real-time 22 kV SCADA system covering a large area of 180 substations. The expert system gives accurate fault analysis for all the fault cases and proposes the best restoration plans in less than 15 seconds. In addition, it advises the control centre operators in cases of emergency, helps them in maintenance planning and can be used as a training simulator in offline mode.

The Penn State intelligent distributed control research laboratory

The authors describe the intelligent distributed controls research laboratory in the College of Engineering at Pennsylvania State University (PSU). The centerpiece of equipment is a modern distributed microprocessor-based control system which was interfaced to real-time simulations of power plant processes. Implementation issues of hierarchical and distributed control for large-scale power plant systems were more fully explored at the university level. The microprocessor-based control system has also been interfaced to the PSU TRIGA nuclear research reactor and enables research in optimal, robust, intelligent, and other advanced control techniques for nuclear power plants. >

Software tools for distributed intelligent control systems

The future of intelligent control systems depends upon the extent to which Artificial Intelligence (AI) technology can help control engineers deliver practical solutions to difficult control engineering problems. Conventional control design approaches have achieved notable successes in the design and implementation of robust, adaptive controllers for systems with well-defined mathematical models. However, conventional approaches have had difficulty supporting engineers in the design and implementation of control systems when an accurate mathematical model is not available. Also, verification that computer-controlled systems perform to specifications, validation of the specifications, higher-level control, operator decision aids, system diagnosis, operator alerting, and reconfiguration of systems which experience large changes over time or potentially catastrophic failures are significant challenges to control science and engineering. It is in these difficult areas where the AI technologies of knowledge representation, learning, search, diagnosis, planning, and decision are being used to aid control engineers. Algorithms for computer-controlled systems and software tools to help implement these algorithms have been a subject of research and commercialization for decades. Computer-Aided Control Engineering (CACE) tools have achieved a degree of success in the past decade based on their ability to assist in the control system design and implementation process. Specialized tools have been made available for system identification, system simulation, controller design and controller implementation. Recently, efforts have been made to build integrated CACE environments. Also, some current research is aimed at increasing the utility of available systems by creating a mathematical basis and a software architecture for efficiently describing complex systems and using these as a means of achieving a higher level of integration of the diverse tools already available. A recent Workshop on Software Tools for Distributed Intelligent Control Systems was sponsored by the U.S. Army and The Defense Advanced Research Projects Agency (DARPA). This paper will describe the results of the workshop and subsequent efforts to use these results to shape a DARPA software development project. The first section of the paper provides a brief review of the current applications of AI in the design and implementation of control systems. The second section discusses areas where AI can be applied in the near term to help solve challenges in the implementation of computer-controlled systems. The third section gives an overview of the development of CACE tools. The fourth section provides a review of the Army/DARPA workshop and the last section discusses the use of the results of the workshop.

NBS/LANL Racetrack Microtron Control System

The distributed intelligence control system for the NBS/LANL racetrack microtron (RTM) is now nearing completion, with all major subsystems implemented and tested, thus providing some operating experience with most of the control system innovations. These include a triple hierarchy of microprocessor-based control elements, consisting of a primary control station and multiple secondary and tertiary control stations; light-link coupling to a tertiary station which operates at a 100 kV potential; a common database shared by separate microprocessors for handling hardware control and operator interactions; and joy stick control of the entire system. A unique secondary station interpreter program was used to great advantage for testing and checkout of various control and monitoring subsystems. The hardware design of the control system is based on Multibus I crates containing commercial Multibus I boards and a few custom designed boards. The primary-secondary data link is a high speed, bidirectional, full-duplex, 8-bit, byte parallel link designed for this application. This link permits very fast updating of the monitored data (>5 per second) and timely response to operator control inputs at the primary station.

Distributed Cement Plant Control - The Intelligent Approach

Rapidly evolving technology has made a broad variety of control system solutions available to industry. Most recently, microprocessors have formed the leading edge of the technology growth. The capability inherent in the present generation microprocessors will not only change the appearance of control plant-equipment, it will in fact change the methods for implementing wide control. The use of microprocessors in a distributed control mode is discussed. The system configuration consists of a central processing unit, intelligent operator's panel, and intelligent distributed field control multiplexers. Each device is described from an operational viewpoint. Special features are presented relative to the system and its applicability for control of a cement plant. In conclusion, intelligent distributed control offer the way to improved control, greater reliability, and lower installed costs.