TECHNIQUES OF MODEL BASED CONTROL

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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 Preface
Acknowledgements. 1. Introduction.
Nature of The Process Control Problem. Overview of Model Based Process Control. Summary.
2. Continuous-Time Models. Introduction. Process Model Representations. Time Domain Models. Laplace Domain Models. FOPDT Models and Model Identification.
3. One-Degree of Freedom Internal Model Control. Introduction. Properties of IMC. IMC Designs for No Disturbance Lag. Design for Processes with No Zeros Near the Imaginary Axis or in the Right Half of the s-Plane. Design for Processes with Zeros Near the Imaginary Axis. Design for Processes with Right Half Plane Zeros. Problems with Mathematically Optimal Controllers. Modifying the Process to Improve Control System Performance. Software Tools for IMC Design. Summary.
4. Two-Degree of Freedom Internal Model Control. Introduction. Structure of Two-Degree of Freedom IMC. Design for Stable Processes. Design for Unstable Processes. Software Tools For 2DF IMC Designs. Summary.
5. Model State Feedback Implementations of IMC Systems.
Motivation. MSF Implementation of 1DF IMC. SIMULINK Realization of MSF Implementation of IMC. Finding A Safe Lower Bound on the MSF Filter Time Constant. MSF Implementation of 2DF IMC. Summary.
6. PI and PID Parameters From IMC Designs. Introduction. The PID Controller. PID Parameters from IMC Controllers.Algorithms and Software For Computing PID Parameters. Accommodating Negative Integral and Derivative Time Constants. 2DF PID Parameters from 2DF IMC. Saturation Compensation. Summary.
7. Tuning and Synthesis of 1DF IMC for Uncertain Processes.
Introduction. Process Uncertainty Descriptions. Mp Tuning. Conditions For Existence of Solutions to The Mp Tuning Problem. Mp Synthesis. Software for Mp Tuning and Synthesis. Summary.
8 Tuning and Synthesis of 2DF IMC for Uncertain Processes.
Introduction. Mp Tuning for Stable Overdamped Uncertain Processes. Mp Synthesis for Stable Overdamped Processes. Mp Tuning for Underdamped and Unstable Processes. Mp Synthesis for Underdamped and Unstable Processes. Summary.
9. Feedforward Control. Introduction. Controller Design when Perfect Compensation is Possible. Controller Design when Perfect Compensation is Not Possible. Controller Design for Uncertain Processes. Summary.
10. Cascade Control. Introduction. Cascade Structures and Controller Designs. Saturation Compensation. Summary.
11. Output Constraint Control (Override Control). Introduction. Override and Cascade Control Structures. Cascade Constraint Control. Summary.
12. Single Variable Inferential Control. Introduction. Classical Control Strategies. Inferential Control. Summary.
13. Inferential Estimation Using Multiple Measurements.
Introduction. Derivation of the Steady State Estimator. Selection of Secondary Measurements. Adding Dynamic Compensation to the Estimator. Optimal Estimation. Summary.
14. Discrete-Time Models. The Z-Tranform Representation. Models of Computer-Controlled Systems. Discrete-Time FIR Models. Discrete-Time FSR Models. Summary.
15. Identification: Basic Concepts. Introduction. Least-Squares Estimation of Parameters. Properties of The Least-Squares Estimator. General Procedure For Process Identification. Summary.
16. Identification: Advanced Concepts. Design of Input Signals: PRBS Signals. Noise Prefiltering. Modifications To The Basic Least-Squares Identification. Multiple Input Multiple Output Systems. A Comprehensive Example. Effect of Prefilter on Parameter Estimates. Software for Identification. Summary.
17. Basic Model Predictive Control. Introduction. SISO MPC. Unconstrained Multivariable Systems. State Space Formulation of Unconstrained MPC. Summary.
18. Advanced Model Predictive Control. Incorporating Constraints. Incorporating Economic Objectives: The LP-MPC Algorithm. Extension to Nonlinear Systems. Extension to Batch Processes. Summary.
19. Inferential MPC. Inferential Model Predictive Control. Simple Regression Estimators. Data-Driven Dynamic Estimators. Nonlinear Data-Driven Estimators. Summary.
Appendices.
A. Review of Basic Concepts. Block Diagrams. Laplace Trasnform and Transfer Functions. P, PI, and PID Controller Transfer Functions. Stability of Systems. Stability of Closed Loop Systems. Controller Tuning. Regulatory Issues Introduced by Constraints.
B. Frequency Response Analysis. Introduction. Frequency Response From Transfer Functions. Disturbance Suppression in SISO Systems: Effect of Constraints. Stability In The Frequency Domain. Closed Loop Frequency Response Characteristics.
C: Review of Linear Least-Squares Regression. Derivation of The Linear Least-Squares Estimate. Properties of The Linear Least-Squares Estimate. Measures of Model Fit. Robustness. Principal Component Regression.
D: Random Variables and Random Processes. Introduction To Random Variables. Random Processes and White Noise. Spectral Decomposition of Random Processes. Multidimensional Random Variables.
E: MATLAB and Control Toolbox Tutorial. MATLAB Resources. Basic Commands. Control System Toolbox Tutorial.
F: SIMULINK Tutorial. Basics. Laplace Transform Models. Simulation of Discrete Systems Using SIMULINK.
G: Tutorial on IMCTUNE Software. Introduction. Getting Started on 1DF Systems. Menu Bar for 1DF Systems. Getting Started on 2DF Systems. Menu Bar for 2DF Systems. Getting Started on Cascade Systems. Menu Bar for Cascade Systems.
H: Identification Software. Introduction. POLYID: Description. MODELBUILDER. PIDTUNER.
I: SIMULINK Models for Case Studies. Naphtha Cracker. Shell Heavy Oil Fractionator. Temperature and Level Control in a Mixing Tank. Pressure and Level Control Experiment. Temperature and Level Control. Heat Exchanger. The Tennessee Eastman Project.

Auteur : BROSIOW
Editeur : PRENTICE HALL
Nombre de pages : 800
Date de publication : 01 2002

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