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

Industrial Control Systems (eBook, PDF)

Mathematical and Statistical Models and Techniques

• Format: PDF

Produktbeschreibung

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 analysisSystems fundamentalsTechnical systemsProduction systemsSystems filtering theorySystems controlLinear and nonlinear systemsSwitching in systemsSystems communicationTransfer systemsStatistical 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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 382
• Erscheinungstermin: 19. April 2016
• Englisch
• ISBN-13: 9781420075595
• Artikelnr.: 38295304

Autorenporträt

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

Inhaltsangabe

Mathematical Modeling for Product Design. Dynamic Fuzzy Systems Modeling.
Stochastic Systems Modeling. Systems Optimization Techniques. Statistical
Control Techniques. Design of Experiment Techniques. Risk Analysis and
Estimation Techniques. Mathematical Modeling and Control of
Multi-Constrained Projects. On-Line Support Vector Regression with Varying
Parameters for Time-Dependent Data. Appendix: Mathematical and Engineering
References.