Introduction to Process Control
Analysis, Mathematical Modeling, Control and Optimization
This textbook is designed for an introductory graduate-level course on process control, commonly found in engineering programs. It emphasizes the statistical techniques and methods required for mathematical modeling, analysis, simulation, control, and optimization of multivariable manufacturing processes. The content is divided into four sections: 1. Relevant mathematical methods, covering random events, variables, estimation, confidence intervals, Bayes applications, regression analysis, statistical cluster analysis, and singular value decomposition. 2. Mathematical descriptions of manufacturing processes, including static and dynamic models, model validation, confidence intervals for parameters, principal component analysis, and various least squares procedures. 3. Control of manufacturing processes, addressing transfer function models, state-variable models, discrete-time classical control methods, state observers, decoupling control, and adaptive control techniques. 4. System optimization methods, discussing unconstrained and constrained optimization, analytical and numerical procedures, penalty functions, linear programming, gradient methods, direct search methods, genetic optimization, and dynamic programming applications. Each section includes end-of-chapter exercises, making the book suitable for systems, electrical, chemical, or industrial engineering programs. Students will learn to develop mathematical model
