Book description
The state-of-the-art publication in model-based process control—by leading experts in the field.
In Techniques of Model-Based Control, two leading experts bring together powerful advances in model-based control for chemical-process engineering. Coleman Brosilow and Babu Joseph focus on practical approaches designed to solve real-world problems, and they offer extensive examples and exercises.
Coverage includes:
The nature of the process-control problem and how model-based solutions help to solve it
Continuous time modeling: time domain, Laplace domain, and FOPDT models
Feedforward, cascade, override, and single-variable inferential control approaches
One and two degree of freedom Internal Model Control
Model State Feedback and PI/PID Implementations of IMC
Tuning and synthesis of 1DF and 2DF IMC for process uncertainty
Estimation and inferential control using multiple secondary measurements
Basic and advanced techniques of model identification and model-predictive control
The appendices review the basics of Laplace transforms, feedback control, frequency response analysis, probability, random variables, and linear least-square regression.
From start to finish, Techniques of Model-Based Control offers the real-world insight that professionals need to identify and implement the best control strategies for virtually any process.
Table of contents
- Copyright
- Prentice Hall International Series in the Physical and Chemical Engineering Sciences
- Preface
- Acknowledgements
- 1. Introduction
- 2. Continuous-Time Models
-
3. One-Degree of Freedom Internal Model Control
- 3.1. Introduction
- 3.2. Properties of IMC
- 3.3. IMC Designs for No Disturbance Lag
- 3.4. Design for Processes with No Zeros Near the Imaginary Axis or in the Right Half of the s-Plane
- 3.5. Design for Processes with Zeros Near the Imaginary Axis
- 3.6. Design for Processes with Right Half Plane Zeros
- 3.7. Problems with Mathematically Optimal Controllers
- 3.8. Modifying the Process to Improve Control System Performance
- 3.9. Software Tools for IMC Design
- 3.10. Summary
- 4. Two-Degree of Freedom Internal Model Control
- 5. Model State Feedback Implementations of IMC
- 6. PI and PID Parameters from IMC Designs
-
7. Tuning and Synthesis of 1DF IMC for Uncertain Processes
- 7.1. Introduction
- 7.2. Process Uncertainty Descriptions
- 7.3. Mp Tuning
-
7.4. Conditions for the Existence of Solutions to the Mp Tuning Problem
- 7.4.1. Statement of the Nyquist Stability Criterion
- 7.4.2. Integral Controllability
- 7.4.3. Necessary and Sufficient Conditions for the Existence of a Solution to the Mp Tuning Problem for Any Mp Specification Greater than One
- 7.4.4. Justification for the Choice of Complementary Sensitivity Function in Mp Tuning
- 7.5. Robust Stability
- 7.6. MP Synthesis
- 7.7. Software for Mp Tuning and Synthesis
- 7.8. Summary
- 8. Tuning and Synthesis of 2DF IMC for Uncertain Processes
- 9. Feedforward Control
- 10. Cascade Control
- 11. Output Constraint Control (Override Control)
- 12. Single Variable Inferential Control (IC)
- 13. Inferential Estimation Using Multiple Measurements
- 14. Discrete-Time Models
- 15. Identification: Basic Concepts
-
16. Identification: Advanced Concepts
- 16.1. Design of Input Signals: PRBS Signals
- 16.2. Noise Prefiltering
- 16.3. Modifications to the Basic Least-Squares Identification
- 16.4. Multiple Input, Multiple Output (MIMO) Systems
- 16.5. A Comprehensive Example
- 16.6. Effect of Prefilter on Parameter Estimates
- 16.7. Software for Identification
- 16.8. Summary
- 17. Basic Model-Predictive Control
-
18. Advanced Model-Predictive Control
- 18.1. Incorporating Constraints
- 18.2. Incorporating Economic Objectives: The LP-MPC Algorithm
- 18.3. Extension to Nonlinear Systems
- 18.4. Extension to Batch Processes
- 18.5. Summary
- 19. Inferential Model-Predictive Control
- A. Review of Basic Concepts
- B. Review of Frequency Response Analysis
- C. Review of Linear Least-Squares Regression
- D. Review of Random Variables and Random Processes
-
E. MATLAB and Control Toolbox Tutorial
- E.1. MATLAB Resources
-
E.2. Basic Commands
- E.2.1. Creating Matrices
- E.2.2. Manipulating Matrices
- E.2.3. Creating Matrices
- E.2.4. Operations on Matrix Elements
- E.2.5. Programming in MATLAB
- E.2.6. File Management, Search Paths
- E.2.7. Storing MATLAB Statements in a File: m-files
- E.2.8. Creating New Functions
- E.2.9. Graphics
- E.2.10. Demonstrations
- E.2.11. Numerical Analysis
- E.2.12. Solution of Ordinary Differential Equations
-
E.3. Control System Toolbox Tutorial
- E.3.1. Entering a System Transfer Function Model
- E.3.2. Conversion to Other Representations
- E.3.3. Analysis of Control Systems
- E.3.4. Block Diagram Analysis
- E.3.5. Root Locus Analysis
- E.3.6. Phase and Gain Margins
- E.3.7. Approximating Time Delays
- E.3.8. Discrete System Modeling
- E.3.9. Analysis of Discrete Systems
- E.3.10. Multivariable System Modeling
- E.3.11. Parallel Connections
- E.3.12. Closed-Loop Systems
- Problems
- F. SIMULINK Tutorial
- G. Tutorial on IMCTUNE Software
- H. Identification Software
- I. Simulink Models for Projects
Product information
- Title: Techniques of Model-Based Control
- Author(s):
- Release date: April 2002
- Publisher(s): Pearson
- ISBN: 013028078X
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