Industrial Control Systems Mathematical and Statistical Models, and Techniques

Issues such as logistics, the coordination of different teams, and automatic control of machinery become more difficult when dealing with large, complex projects. Yet all these activities have common elements and can be represented by mathematics. Linking theory to practice, Industrial Control Systems: Mathematical and Statistical Models and Techniques presents the mathematical foundation for building and implementing industrial control systems. The book contains mathematically rigorous models and techniques generally applicable to control systems with specific orientation toward industrial systems. An amalgamation of theoretical developments, applied formulations, implementation processes, and statistical control, the book covers: Industrial innovations and systems analysis Systems fundamentals Technical systems Production systems Systems filtering theory Systems control Linear and nonlinear systems Switching in systems Systems communication Transfer systems Statistical experimental design models (factorial design and fractional factorial design) Response surface models (central composite design and Box–Behnken design) Examining system fundamentals and advanced topics, the book includes examples that demonstrate how to use the statistical designs to develop feedback controllers and minimum variance controller designs for industrial applications. Clearly detailing concepts and step-by-step procedures, it matches mathematics with practical applications, giving you the tools to achieve system control goals.

Mathematical Modeling for Product DesignIntroductionLiterature ReviewMemetic Algorithm and Its Application to Collaborative DesignConclusionReferencesDynamic Fuzzy Systems ModelingIntroduction: Decision Support Systems, UncertaintiesDecision Support SystemsUncertaintyFuzzinessFuzzy Set SpecificationsStochastic–Fuzzy ModelsApplicationsConclusionsReferencesStochastic Systems ModelingIntroduction to Model TypesSystems Filtering and EstimationCorrelation TechniquesModel Control—Model Reduction, Model AnalysisReferencesSystems Optimization TechniquesOptimality ConditionsBasic Structure of Local MethodsStochastic Central ProblemsIntelligent Heuristic ModelsHeuristicsReferencesStatistical Control TechniquesStatistical Process ControlControl ChartsProcess Capability AnalysisTime Series Analysis and Process EstimationTime Series Analysis ExampleExponentially Weighted Moving AverageCumulative Sum Chart Statistical Process ControlAutomatic Process ControlCriticisms of SPC And APCOvercompensation, Disturbance Removal, and Information ConcealingIntegration Of SPC And APCSystems Approach To Process AdjustmentARIMA Modeling Of Process DataModel Identification And EstimationMinimum Variance ControlProcess Dynamics With DisturbanceReferencesDesign of Experiment TechniquesFactorial DesignsFactorial Design for 3 FactorsSaturated DesignsCentral Composite DesignsResponse Surface OptimizationReferencesRisk Analysis and Estimation TechniquesBayesian Estimation ProcedureComputational ProcedureParameter Estimation for Hyperbolic Decline CurveRobustness of Decline CurvesMathematical AnalysisStatistical AnalysisParameter EstimationOptimization TechniqueIterative Procedure Residual Analysis TestSimplified Solution to the Vector EquationIntegrating Neural Networks And Statistics For Process Control4Fundamentals of Neural NetworkThe Input FunctionTransfer FunctionsStatistics and Neural Networks PredictionsStatistical Error AnalysisIntegration of Statistics And Neural NetworksReferencesMathematical Modeling and Control of Multi- Constrained ProjectsIntroductionLiterature ReviewMethodologyRepresentation of Resource Interdependencies and MultifunctionalityModeling of Resource CharacteristicsResource MapperActivity SchedulerModel Implementation And Graphical IllustrationsNotationsReferencesOnline Support Vector Regression with Varying Parameters for Time-Dependent DataIntroductionModified Gompertz Weight Function for Varying SVR ParametersAccurate Online SVR with Varying ParametersExperimental ResultsConclusionReferencesAppendix: Mathematical and Engineering ReferencesIndex

Adedeji B. Badiru, Oye Ibidapo-Obe, Babatunde J. Ayeni