Distributed sensor architecture for intelligent control that supports quality of control and quality of service

This paper is part of a study of intelligent architectures for distributed control and communications systems. The study focuses on optimizing control systems by evaluating the performance of middleware through quality of service (QoS) parameters and the optimization of control using Quality of Control (QoC) parameters. The main aim of this work is to study, design, develop, and evaluate a distributed control architecture based on the Data-Distribution Service for Real-Time Systems (DDS) communication standard as proposed by the Object Management Group (OMG). As a result of the study, an architecture called Frame-Sensor-Adapter to Control (FSACtrl) has been developed. FSACtrl provides a model to implement an intelligent distributed Event-Based Control (EBC) system with support to measure QoS and QoC parameters. The novelty consists of using, simultaneously, the measured QoS and QoC parameters to make decisions about the control action with a new method called Event Based Quality Integral Cycle. To validate the architecture, the first five Braitenberg vehicles have been implemented using the FSACtrl architecture. The experimental outcomes, demonstrate the convenience of using jointly QoS and QoC parameters in distributed control systems.The study described in this paper is a part of the coordinated project COBAMI: Mission-based Hierarchical Control. Education and Science Department Spanish Government. CICYT: MICINN: DPI2011-28507-C02-01/02 and project "Real time distributed control systems" of the Support Program for Research and Development 2012 UPV (PAID-06-12).Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE.; Simarro Fernández, R.; Benet Gilabert, G. (2015). Distributed sensor architecture for intelligent control that supports quality of control and quality of service. Sensors. 15(3):4700-4733. https://doi.org/10.3390/s150304700S4700473315

Manual control models of industrial management

The industrial engineer is often required to design and implement control systems and organization for manufacturing and service facilities, to optimize quality, delivery, and yield, and minimize cost. Despite progress in computer science most such systems still employ human operators and managers as real-time control elements. Manual control theory should therefore be applicable to at least some aspects of industrial system design and operations. Formulation of adequate model structures is an essential prerequisite to progress in this area; since real-world production systems invariably include multilevel and multiloop control, and are implemented by timeshared human effort. A modular structure incorporating certain new types of functional element, has been developed. This forms the basis for analysis of an industrial process operation. In this case it appears that managerial controllers operate in a discrete predictive mode based on fast time modelling, with sampling interval related to plant dynamics. Successive aggregation causes reduced response bandwidth and hence increased sampling interval as a function of level

Security issues and quality of service in real time wireless PLC/SCADA process control systems

Abstract: A wireless PLC/SCADA network has been set up to investigate the reliability of wireless communication systems in a local area network. It has been shown that the integrity of data flow can be maintained within certain limits of the signal strength in a coverage area of an Access Point. The Wi-Fi can successfully be applied in industrial operations provided a careful site surveys has been be conducted and the boundaries are determined to ensure adequate signal strength to avoid any possible dropouts however short lived they may be. It has also been shown that if the dropout occurs the self repair capability of the communication system may not be sufficient thus requiring manual interference that may not be tolerable in many process control operations. (15 refs.

Multi-Agent Architecture with Support to Quality of Service and Quality of Control

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-15381-5_17Multi Agent Systems (MAS) are one of the most suitable frameworks for the implementation of intelligent distributed control system. Agents provide suitable flexibility to give support to implied heterogeneity in cyber-physical systems. Quality of Service (QoS) and Quality of Control (QoC) parameters are commonly utilized to evaluate the efficiency of the communications and the control loop. Agents can use the quality measures to take a wide range of decisions, like suitable placement on the control node or to change the workload to save energy. This article describes the architecture of a multi agent system that provides support to QoS and QoC parameters to optimize de system. The architecture uses a Publish-Subscriber model, based on Data Distribution Service (DDS) to send the control messages. Due to the nature of the Publish-Subscribe model, the architecture is suitable to implement event-based control (EBC) systems. The architecture has been called FSACtrlThe architecture described in this article is a part of the coordinated project SIDIRELI: Distributed Systems with Limited Resources. Control Kernel and Coordination. Education and Science Department, Spanish Government and FEDER funds.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2010). Multi-Agent Architecture with Support to Quality of Service and Quality of Control. En Intelligent Data Engineering and Automated Learning – IDEAL 2010. Springer Verlag (Germany). 137-144. doi:10.1007/978-3-642-15381-5_17S137144Lee, E.A.: Cyber Physical Systems: Design Challenges. In: 11th IEEE Symposium on Object Oriented Real-Time Distributed Computing, pp. 363–369 (2008)Siegel, J.: CORBA 3: Fundamentals and Programming. OMG (2000)FIPA. FIPA-QoS (2002), http://www.fipa.org/specs/fipa00094Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal (2005-12-04)Posadas, J.L., Poza, J.L., Simó, J.E., Benet, G., Blanes, F.: Agent Based Distributed Architecture for Mobile Robot Control. Engineering Applications of Artificial Intelligence 21(6), 805–823 (2008)Aurrecoechea, C., Campbell, A.T., Hauw, L.: A Survey of QoS Architectures. Multimedia Systems Journal, Special Issue on QoS Architecture 6(3), 138–151 (1998)Pardo-Castellote, G.: OMG Data-Distribution Service: architectural overview. In: Proceedings of 23rd International Conference on Distributed Computing Systems Workshops, Providence, USA, vol. 19(22), pp. 200–206 (2003)International Telecommunication Union (ITU). Terms and Definitions Related to Quality of Service and Network Performance Including Dependability. ITU-T Recommendation E.800 (0894) (1994)Sánchez, J., Guarnes, M.Á., Dormido, S.: On the Application of Different Event-Based Sampling Strategies to the Control of a Simple Industrial Process. Sensors 9, 6795–6818 (2009)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall, Englewood Cliffs (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: Middleware with QoS Support to Control Intelligent Systems. In: 2th International Conference on Advanced Engineering Computing and Applications in Sciences, ADVCOMP, pp. 211–216 (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009

Dynamic Wireless QoS Analysis for Real-Time Control in URLLC

One of the major goals of ultra-reliable and low-latency communication (URLLC) is to enable real-time wireless control systems. However, it is challenging to use URLLC throughout the control process since a huge amount of wireless resource is needed to maintain the rigorous quality-of-service (QoS) in URLLC, i.e, ultra reliability and low latency. In this paper, our goal is to discuss that whether the extreme high QoS in URLLC leads to better control performance than low QoS during the control process. This is expected to provide a guideline on the usage of the URLLC throughout the control process dynamically. Specifically, we first investigate the relationship between the URLLC QoS and control performance. Then, we discuss the effect of different communication QoS on the control performance. Our results show that the rigorous QoS in URLLC and a low QoS can be used dynamically throughout the control process with high system performance

Automatic Generation Control and its Implementation in Real Time

In power systems, the control mechanism responsible for maintaining the system frequency to the nominal value and the real power interchange between balancing authority areas to the scheduled values is referred to as automatic generation control (AGC). The purpose of this paper is to present a systematic way to determine, in real time, the power allocated to each generator participating in AGC by taking into account the cost and quality of the AGC service provided. To this end, we formulate the economic dispatch process and gain insights into the economic characteristics of the generating units. We value the quality of AGC service by taking into consideration the ramping constraints of the generating units. The proposed methodology is illustrated in the WECC system and is compared with other allocation methods

Adaptive Quality of Service Control in Distributed Real-Time Embedded Systems

An increasing number of distributed real-time embedded systems face the critical challenge of providing Quality of Service (QoS) guarantees in open and unpredictable environments. For example, such systems often need to enforce CPU utilization bounds on multiple processors in order to avoid overload and meet end-to-end dead-lines, even when task execution times deviate significantly from their estimated values or change dynamically at run-time. This dissertation presents an adaptive QoS control framework which includes a set of control design methodologies to provide robust QoS assurance for systems at different scales. To demonstrate its effectiveness, we have applied the framework to the end-to-end CPU utilization control problem for a common class of distributed real-time embedded systems with end-to-end tasks. We formulate the utilization control problem as a constrained multi-input-multi-output control model. We then present a centralized control algorithm for small or medium size systems, and a decentralized control algorithm for large-scale systems. Both algorithms are designed systematically based on model predictive control theory to dynamically enforce desired utilizations. We also introduce novel task allocation algorithms to ensure that the system is controllable and feasible for utilization control. Furthermore, we integrate our control algorithms with fault-tolerance mechanisms as an effective way to develop robust middleware systems, which maintain both system reliability and real-time performance even when the system is in face of malicious external resource contentions and permanent processor failures. Both control analysis and extensive experiments demonstrate that our control algorithms and middleware systems can achieve robust utilization guarantees. The control framework has also been successfully applied to other distributed real-time applications such as end-to-end delay control in real-time image transmission. Our results show that adaptive QoS control middleware is a step towards self-managing, self-healing and self-tuning distributed computing platform

Relationship between quality of control and quality of service in mobile robot navigation

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-28765-7_67This article presents the experimental work developed to test the viability and to measure the efficiency of an intelligent control distributed architecture. To do this, a simulated navigation scenario of Braitenberg vehicles has been developed. To test the efficiency, the architecture uses the performance as QoS parameter. The measuring of the quality of the navigation is done through the ITAE QoC parameter. Tested scenarios are: an environment without QoS and QoC man-aging, an environment with a relevant message filtering and an environment with a predictive filtering by the type of control. The results obtained show that some of the processing performed in the control nodes can be moved to the middleware to optimize the robot navigation.The work described in this article is a part of the coordinated project SIDIRELI: (Distributed Systems with Limited Resources) and COBAMI (Mission-Based Control) Education and Science Department, Spanish Government and European FEDER found. MICINN CICYT: SIDIRELI: DPI2008-06737-C02-01/02, COBAMI: DPI2011-28507-C02-02.Poza-Lujan, J.; Posadas-Yagüe, J.; Simó Ten, JE. (2012). Relationship between quality of control and quality of service in mobile robot navigation. En Distributed Computing and Artificial Intelligence: 9th International Conference. Springer. 557-564. https://doi.org/10.1007/978-3-642-28765-7_67S557564Vogel, A., Kerherve, B., von Bochmann, G., Gecsei, J.: Distributed Multimedia and QoS: A Survey. IEEE Multimedia 2(2), 10–19 (1995)Crawley, E., Nair, R., Rajagopalan, B.: RFC 2386: A Framework for QoS-based Routing in the Internet. IETF Internet Draft, 1–37 (1998)Bradner, S.: RFC 2026: The Internet Standards Process. IETF Internet Draft, sec.10 (1996)Object Management Group (OMG): Data Distribution Service for Real-Time Systems, v1.1. Document formal (April 12, 2005)Poza, J.L., Posadas, J.L., Simó, J.E.: QoS-based middleware architecture for distributed control systems. In: International Symposium on Distributed Computing and Artificial Intelligence. DCAI, Salamanca, Spain (2008)Poza, J.L., Posadas, J.L., Simó, J.E.: A Survey on Quality of Service Support on Middleware-Based Distributed Messaging Systems Used in Multi Agent Systems. In: 9th International Conference on Practical Applications of Agents and Multi-Agent Systems. DCAI, Salamanca, Spain (2011)Dorf, R.C., Bishop, R.H.: Modern Control Systems, 11th edn. Prentice Hall (2008)Soucek, S., Sauter, T.: Quality of Service Concerns in IPBased Control Systems. IEEE Transactions on Industrial Electronics 51(6) (December 2004)Poza, J.L., Posadas, J.L., Simó, J.E.: Multi-Agent Architecture with Support to Quality of Service and Quality of Control. In: 11th International Conference on Intelligent Data Engineering and Automated Learning, Paisley, UK (2010)Braitenberg, V.: Vehicles: Experiments on Synthetic Psychology. MIT Press, Cambridge (1984)Gabel, O., Litz, L.: QoS-adaptive Control in NCS with Variable Delays and Packet Losses – A Heuristic Approach. In: 43rd IEEE Conference on Decision and Control (2004)Poza, J.L., Posadas, J.L., Simó, J.E.: From the Queue to the Quality of Service Policy: A Middleware Implementation. In: Omatu, S., Rocha, M.P., Bravo, J., Fernández, F., Corchado, E., Bustillo, A., Corchado, J.M. (eds.) IWANN 2009, Part II. LNCS, vol. 5518, pp. 432–437. Springer, Heidelberg (2009