I AUTOMATION & CONTROL - Theory and Practice AUTOMATION & CONTROL - Theory and Practice Edited by A. D. Rodić In-Tech intechweb.org Published by In-Teh In-Teh Olajnica 19/2, 32000 Vukovar, Croatia Abstracting and non-prot use of the material is permitted with credit to the source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained inside. After this work has been published by the In-Teh, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2009 In-teh www.intechweb.org Additional copies can be obtained from: publication@intechweb.org First published December 2009 Printed in India Technical Editor: Melita Horvat AUTOMATION & CONTROL - Theory and Practice, Edited by A. D. Rodić p. cm. ISBN 978-953-307-039-1 V Preface Automation is the use of control systems (such as numerical control, programmable logic control, and other industrial control systems), in concert with other applications of information technology (such as computer-aided technologies [CAD, CAM, CAx]), to control industrial machinery and processes, reducing the need for human intervention. In the scope of industrialization, automation is a step beyond mechanization. Whereas mechanization provided human operators with machinery to assist them with the muscular requirements of work, automation greatly reduces the need for human sensory and mental requirements as well. Processes and systems can also be automated. Automation plays an increasingly important role in the global economy and in daily experience. Engineers strive to combine automated devices with mathematical and organizational tools to create complex systems for a rapidly expanding range of applications and human activities. Many roles for humans in industrial processes presently lie beyond the scope of automation. Human-level pattern recognition, language recognition, and language production ability are well beyond the capabilities of modern mechanical and computer systems. Tasks requiring subjective assessment or synthesis of complex sensory data, such as scents and sounds, as well as high-level tasks such as strategic planning, currently require human expertise. In many cases, the use of humans is more cost-effective than mechanical approaches even where automation of industrial tasks is possible. Specialized industrial computers, referred to as programmable logic controllers (PLCs), are frequently used to synchronize the ow of inputs from (physical) sensors and events with the ow of outputs to actuators and events. This leads to precisely controlled actions that permit a tight control of almost any industrial process. Human-machine interfaces (HMI) or computer human interfaces (CHI), formerly known as man-machine interfaces, are usually employed to communicate with PLCs and other computers, such as entering and monitoring temperatures or pressures for further automated control or emergency response. Service personnel who monitor and control these interfaces are often referred to as stationary engineers. Different types of automation tools exist: • ANN - Articial neural network • DCS - Distributed Control System • HMI - Human Machine Interface • SCADA - Supervisory Control and Data Acquisition VI • PLC - Programmable Logic Controller • PAC - Programmable Automation Controller • Instrumentation • Motion control • Robotics Control theory is an interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. Control theory is • a theory that deals with inuencing the behavior of dynamical systems • an interdisciplinary subeld of science, which originated in engineering and mathematics, and evolved into use by the social, economic and other sciences. Main control techniques assume: • Adaptive control uses on-line identication of the process parameters, or modication of controller gains, thereby obtaining strong robustness properties. • A Hierarchical control system is a type of Control System in which a set of devices and governing software is arranged in a hierarchical tree. When the links in the tree are implemented by a computer network, then that hierarchical control system is also a form of a Networked control system. • Intelligent control use various AI computing approaches like neural networks, Bayesian probability, fuzzy logic, machine learning, evolutionary computation and genetic algorithms to control a dynamic system. • Optimal control is a particular control technique in which the control signal optimizes a certain “cost index”. Two optimal control design methods have been widely used in industrial applications, as it has been shown they can guarantee closed-loop stability. These are Model Predictive Control (MPC) and Linear-Quadratic-Gaussian control (LQG). • Robust control deals explicitly with uncertainty in its approach to controller design. Controllers designed using robust control methods tend to be able to cope with small differences between the true system and the nominal model used for design. • Stochastic control deals with control design with uncertainty in the model. In typical stochastic control problems, it is assumed that there exist random noise and disturbances in the model and the controller, and the control design must take into account these random deviations. The present edited book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the eld. Chapters contribute to diverse facets of automation and control. The volume is organized in four parts according to the main subjects, regarding the recent advances in this eld of engineering. The rst thematic part of the book is devoted to automation. This includes solving of assembly line balancing problem and design of software architecture for cognitive assembling in production systems. The second part of the book concerns with different aspects of modeling and control. This includes a study on modeling pollutant emission of diesel engine, development of a PLC program obtained from DEVS model, control networks for digital home, automatic control of temperature and ow in heat exchanger, and non-linear analysis and design of phase locked loops. VII The third part addresses issues of parameter estimation and lter design, including methods for parameters estimation, control and design of the wave digital lters. The fourth part presents new results in the intelligent control. That includes building of a neural PDF strategy for hydroelectric station simulator, intelligent network system for process control, neural generalized predictive control for industrial processes, intelligent system for forecasting, diagnosis and decision making based on neural networks and self- organizing maps, development of a smart semantic middleware for the Internet , development appropriate AI methods in fault-tolerant control, building expert system in rotary railcar dumpers, expert system for plant asset management, and building of a image retrieval system in heterogeneous database. The content of this thematic book admirably reects the complementary aspects of theory and practice which have taken place in the last years. Certainly, the content of this book will serve as a valuable overview of theoretical and practical methods in control and automation to those who deal with engineering and research in this eld of activities. The editors are greatfull to the authors for their excellent work and interesting contributions. Thanks are also due to the renomeus publisher for their editorial assistance and excellent technical arrangement of the book. December, 2009 A. D. Rodić IX Contents Preface V I. Automation 1. AssemblyLineBalancingProblemSingleandTwo-SidedStructures 001 WaldemarGrzechca 2. ASoftwareArchitectureforCognitiveTechnicalSystemsSuitableforan AssemblyTaskinaProductionEnvironment 013 EckartHauck,ArnoGramatkeandKlausHenning II. Modeling and Control 3. Twostageapproachesformodelingpollutantemissionofdieselenginebasedon Krigingmodel 029 ElHassaneBrahmi,LilianneDenis-Vidal,ZohraCher,NassimBoudaoudandGhislaine Joly-Blanchard 4. AnapproachtoobtainaPLCprogramfromaDEVSmodel 047 HyeongT.Park,KilY.Seong,SurajDangol,GiN.WangandSangC.Park 5. Aframeworkforsimulatinghomecontrolnetworks 059 RafaelJ.Valdivieso-Sarabia,JorgeAzorín-López,AndrésFuster-GuillóandJuanM.García- Chamizo 6. ComparisonofDefuzzicationMethods:AutomaticControlofTemperatureand FlowinHeatExchanger 077 AlvaroJ.ReyAmaya,OmarLengerke,CarlosA.Cosenza,MaxSuellDutraandMagda J.M.Tavera 7. NonlinearAnalysisandDesignofPhase-LockedLoops 089 G.A.Leonov,N.V.KuznetsovandS.M.Seledzhi III. Estimation and Filter Design 8. Methodsforparameterestimationandfrequencycontrolofpiezoelectric transducers 115 ConstantinVolosencu 9. DesignoftheWaveDigitalFilters 137 BohumilPsenicka,FranciscoGarcíaUgaldeandAndrésRomeroM. X IV. Intelligent Control 10. NeuralPDFControlStrategyforaHydroelectricStationSimulator 161 GermanA.Munoz-Hernandez,CarlosA.Gracios-Marin,AlejandroDiaz-Sanchez,SaadP. MansoorandDewiI.Jones 11. IntelligentNetworkSystemforProcessControl:Applications,Challenges, Approaches 177 QurbanAMemon 12. NeuralGeneralizedPredictiveControlforIndustrialProcesses 199 SadhanaChidrawar,BalasahebPatreandLaxmanWaghmare 13. Forecasting,DiagnosisandDecisionMakingwithNeuralNetworksand Self-OrganizingMaps 231 KazuhiroKohara,KatsuyoshiAokiandMamoruIsomae 14. ChallengesofMiddlewarefortheInternetofThings 247 MichalNagy,ArtemKatasonov,OleksiyKhriyenko,SergiyNikitin,MichalSzydłowskiand VaganTerziyan 15. ArticialIntelligenceMethodsinFaultTolerantControl 271 LuisE.GarzaCastañónandAdrianaVargasMartínez 16. ARealTimeExpertSystemForDecisionMakinginRotaryRailcarDumpers 297 OsevaldoFarias,SoaneLabidi,JoãoFonsecaNeto,JoséMouraandSamyAlbuquerque 17. ModularandHybridExpertSystemforPlantAssetManagement 311 MarioThronandNicoSuchold 18. ImageRetrievalSysteminHeterogeneousDatabase 327 KhalifaDjemal,HichemMaarefandRostomKachouri [...]... Science, Vol 32, No 8, pp 90 9-9 32 12 AUTOMATION & CONTROL - Theory and Practice Erel, E., Sarin S.C (1998) A survey of the assembly line balancing procedures, Production Planning and Control, Vol 9, No 5, pp 41 4-4 34 Fonseca D.J., Guest C.L., Elam M., Karr C.L (2005) A fuzzy logic approach to assembly line balancing, Mathware & Soft Computing, Vol 12, pp 5 7-7 4 Grzechca W (2008) Two-sided assembly line Estimation... to IUFF group are: IUFF-RPW Immediate Update First Fit – Ranked Positional Weight, 4 AUTOMATION & CONTROL - Theory and Practice IUFF-NOF Immediate Update First Fit – Number of Followers, IUFF-NOIF Immediate Update First Fit – Number of Immediate Followers, IUFF-NOP Immediate Update First Fit – Number of Predecessors, IUFF-WET Immediate Update First Fit – Work Element Time 3.2 Two-sided Assembly Line... ample – IUFF Ran nked Positional W Weight Fig 4 Assembly lin balance for IUF g ne FF-RPW and IUFF-NOF methods Assembly Line Balancing Problem Single and Two-Sided Structures 9 Fig 5 Assembly line balance for IUFF-NOP and IUFF-NOIF methods Fig 6 Assembly line balance for IUFF-WET method Method K IUFF-RPW 5 IUFF-NOF 5 IUFF-NOIF 6 Balance S1 – 1, 3, 2 S2 – 6, 4, 8 S3 – 7, 9 S4 – 10, 5 S5 – 11, 12 S1 – 1,... Station (n-3) Station (n-1) Station (n-2) Station n Conveyor Station 2 Station 4 Fig 1 Two-sided assembly line structure Let us consider, for example, a truck assembly line Installing a gas tank, air filter, and toolbox can be more easily achieved at the left-hand side of the line, whereas mounting a battery, air tank, and muffler prefers the right-hand side Assembling an axle, propeller shaft, and radiator... Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics ICINCO 2008, Final book of Abstracts and Proceedings, Funchal, 1 1-1 5 May 2008, pp 8 7-8 8, CD Version ISBN: 97 8-9 8 9-8 11 1-3 5-7 Gutjahr, A.L., Neumhauser G.L (1964) An algorithm for the balancing problem, Management Science, Vol 11,No 2, pp 30 8-3 15 Helgeson W B., Birnie D P (1961) Assembly line balancing using... where: SIL- smoothness index of the left side of two-sided line STmaxL- maximum of duration time of left allocated stations STiL- duration time of i-th left allocated station Smoothness index of the right side SI R K ST i 1 maxR ST iR 2 where: SIR- smoothness index of the right side of two-sided line, STmaxR- maximum of duration time of right allocated stations, STiR- duration time of i-th right... program Gutjahr and Nemhauser (Gutjahr & Nemhauser, 1964) showed that the ALBP problem falls into the class of NP-hard combinatorial optimization problems This means that an optimal solution is not guaranteed for problems of significant size Therefore, heuristic methods have become the most popular techniques for solving the problem Author of this book chapter 2 AUTOMATION & CONTROL - Theory and Practice. .. products in 14 AUTOMATION & CONTROL - Theory and Practice small batch sizes with costs competitive to mass production under the full exploitation of the respective benefits of value orientation and planning orientation To reach the vision four core research areas were identified These areas are “Individualized Production Systems”, “Virtual Production Systems”, “Hybrid Production Systems” and “Self-optimizing... to process different sensor inputs (visual, tactile or electric sensors) and aggregate them to extract essential information Based on this information, the Cognitive Technical System must process the information and find the next best action concerning the current 18 AUTOMATION & CONTROL - Theory and Practice environmental state and the given objective To change the environment according to the next... Machine Interface 22 AUTOMATION & CONTROL - Theory and Practice 5.3 Planning Layer The Planning Layer contains the core elements that are responsible for decision-finding It contains the Kernel and the Cognitive Processor as components The Kernel distributes the signal flows in the Planning Layer The Cognitive Processor computes the next best action u* based on the goal state g* and the current world . I AUTOMATION & CONTROL - Theory and Practice AUTOMATION & CONTROL - Theory and Practice Edited by A. D. Rodić In-Tech intechweb.org Published by In-Teh In-Teh Olajnica. Melita Horvat AUTOMATION & CONTROL - Theory and Practice, Edited by A. D. Rodić p. cm. ISBN 97 8-9 5 3-3 0 7-0 3 9-1 V Preface Automation is the use of control systems (such as numerical control, programmable. Author of this book chapter 1 AUTOMATION & CONTROL - Theory and Practice2 underlines the importance of the final results estimation and proposes for single and two- sided assembly line balancing