1st Edition

Industrial Control Systems Mathematical and Statistical Models and Techniques

    382 Pages 131 B/W Illustrations
    by CRC Press

    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 Design
    Literature Review
    Memetic Algorithm and Its Application to Collaborative Design

    Dynamic Fuzzy Systems Modeling
    Introduction: Decision Support Systems, Uncertainties
    Decision Support Systems
    Fuzzy Set Specifications
    Stochastic–Fuzzy Models

    Stochastic Systems Modeling
    Introduction to Model Types
    Systems Filtering and Estimation
    Correlation Techniques
    Model Control—Model Reduction, Model Analysis

    Systems Optimization Techniques
    Optimality Conditions
    Basic Structure of Local Methods
    Stochastic Central Problems
    Intelligent Heuristic Models

    Statistical Control Techniques
    Statistical Process Control
    Control Charts
    Process Capability Analysis
    Time Series Analysis and Process Estimation
    Time Series Analysis Example
    Exponentially Weighted Moving Average
    Cumulative Sum Chart
    Statistical Process Control
    Automatic Process Control
    Criticisms of SPC And APC
    Overcompensation, Disturbance Removal, and Information Concealing
    Integration Of SPC And APC
    Systems Approach To Process Adjustment
    ARIMA Modeling Of Process Data
    Model Identification And Estimation
    Minimum Variance Control
    Process Dynamics With Disturbance

    Design of Experiment Techniques
    Factorial Designs
    Factorial Design for 3 Factors
    Saturated Designs
    Central Composite Designs
    Response Surface Optimization

    Risk Analysis and Estimation Techniques
    Bayesian Estimation Procedure
    Computational Procedure
    Parameter Estimation for Hyperbolic Decline Curve
    Robustness of Decline Curves
    Mathematical Analysis
    Statistical Analysis
    Parameter Estimation
    Optimization Technique
    Iterative Procedure
    Residual Analysis Test
    Simplified Solution to the Vector Equation
    Integrating Neural Networks And Statistics For Process Control4
    Fundamentals of Neural Network
    The Input Function
    Transfer Functions
    Statistics and Neural Networks Predictions
    Statistical Error Analysis
    Integration of Statistics And Neural Networks

    Mathematical Modeling and Control of Multi- Constrained Projects
    Literature Review
    Representation of Resource Interdependencies and Multifunctionality
    Modeling of Resource Characteristics
    Resource Mapper
    Activity Scheduler
    Model Implementation And Graphical Illustrations

    Online Support Vector Regression with Varying Parameters for Time-Dependent Data
    Modified Gompertz Weight Function for Varying SVR Parameters
    Accurate Online SVR with Varying Parameters
    Experimental Results
    Appendix: Mathematical and Engineering References


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