Industrial Process Identification and Control

Author: Li Shaoyuan Cai Wenjian
Publisher:
Publish Date: 2005-05-01
Features: Introduction to Industrial Process Identification and Control is an important professional course for undergraduate students in automation and graduate students in control science and engineering. This book addresses the requirements of existing control theories and methods for industrial process systems in identification and control, focusing on recent advancements in recent years both domestically and internationally. It integrates the methods of identification, control, and optimization in control theory with the characteristics of process systems, emphasizing how to use control theory methods to analyze and design practical industrial process systems. The book consists of 12 chapters, broadly divided into three parts. Part 1 covers Chapters 1-4, primarily introducing the dynamic characteristics and system structure of process control systems, including the structure of PID controllers, main methods for analyzing control systems, dynamic characteristics of process control systems, and basic process system structures such as series and feedforward. Part 2 covers Chapters 5-9, focusing on system identification methods for single-variable and multi-variable systems from the perspective of practical applications, which can be tolerated by industrial field operations through relay feedback and step tests. Part 3 covers Chapters 10-12, primarily analyzing methods for input/output pairing analysis, coupling analysis, and decentralized controller design for multi-variable control systems, as well as system stability analysis. This book is suitable for teachers, graduate students, and senior undergraduate students in control science and engineering, computer control, systems engineering, and information engineering at universities and colleges, and can also be referenced by relevant technical personnel.
Table of Contents
Table of Contents
1 Basic Concepts of Process Control
1.1 Industrial Process Control Systems
1.2 PID Control
1.2.1 Proportional Action
1.2.2 Integral Action
1.2.3 Derivative Action
1.2.4 Stability of Closed-Loop Systems
1.3 Time-Domain Methods for Controller Design
1.4 Frequency-Domain Methods for Controller Design
1.4.1 Controller Design Based on Frequency Response and Steady-State Gain
1.4.2 Controller Design Using Frequency Response Criteria
2 Advanced Process Control
2.1 Structure of Advanced Process Control Systems
2.1.1 Direct Synthesis
2.1.2 Internal Model Control Approximation Model Adjustment
2.2 Integral Saturation Phenomenon and Anti-Saturation Strategies in Process Control Systems
2.2.1 Input Constraints
2.2.2 Feedback Compensation
2.2.3 Realizable Reference Values
2.2.4 Conditional Integration
2.3 Parameter Tuning of Advanced PID Controllers
2.3.1 Graphical Method
2.3.2 Two-Point Method
2.3.3 Area Method
2.4 Relay Feedback
3 Controller Design for Complex Dynamic Systems
3.1 Dynamic Characteristics of Complex Processes
3.2 Control of Time-Delay Systems
3.2.1 Design of Conventional Feedback Controllers
3.2.2 Smith Predictor
3.2.3 Improved Smith Predictor
3.3 Negative Response Systems
3.3.1 Control of Negative Response Systems
3.3.2 Negative Response Compensation
3.4 Open-Loop Unstable Systems
3.4.1 Difficulties in Control System Design
3.4.2 Two-Step Design Method
4 Complex Control Systems
4.1 Basic Concepts
4.2 Cascade Control Systems
4.2.1 Basic Principles of Cascade Control
4.2.2 Parameter Tuning of Cascade Controllers
4.2.3 Anti-Integral Saturation in Cascade Control Systems
4.3 Feedforward Control
4.3.1 Design of Feedforward Controllers
4.3.2 Practical Considerations
4.3.3 Feedback/Feedforward Control
4.4 Ratio Control
4.5 Single Input Controlling Multiple Outputs
4.6 Multiple Inputs Controlling Single Output
4.7 Inferential Control
4.7.1 Feedback Control Methods
4.7.2 Cascade Control
4.7.3 Estimator-Based Control
4.7.4 Inferential Control
5 Empirical Modeling and Identification of Industrial Process Systems
5.1 Basic Concepts
5.1.1 Basic Definition of Process Identification
5.1.2 Principles of Empirical Modeling
5.2 Least Squares Method
5.2.1 Linear Methods
5.2.2 Linearized Models
5.2.3 Weighted Least Squares
5.2.4 Recursive Least Squares
5.2.5 Exponential Windowed Least Squares
5.3 Fourier Theory
5.3.1 Fourier Transform
5.3.2 Properties of Fourier Transform
5.3.3 Discrete Fourier Transform (DFT)
5.3.4 Fast Fourier Transform (FFT)
5.4 Describing Function
5.4.1 Basic Concepts
5.4.2 Describing Function Estimation
5.4.3 Typical Nonlinear Elements
5.4.4 Limit Cycles
6 Parameter Identification Based on Step Response
6.1 Basic Concepts of Step Response Identification
6.2 Typical Methods for Open-Loop Step Tests
6.2.1 LOG Method
6.2.2 Two-Point Method
6.2.3 Area Method
6.3 Least Squares for Open-Loop Loop Tests
6.4 Classical Closed-Loop Loop Step Tests
6.5 Least Squares Under PID Control
6.5.1 Problem Description
6.5.2 Recursive Solution
6.5.3 Transfer Function Model Identification
6.5.4 Applications and Simulation Examples
7 Parameter Identification Based on Relay Tests
7.1 Basic Principles of Relay Feedback
7.1.1 Generating Stable Oscillations
7.1.2 Estimating Transfer Function
7.1.3 Fourier Transform Method
7.2 Improved Relay Feedback Tests
7.2.1 Asymmetric Switch Feedback
7.2.2 Switch with Hysteresis
7.2.3 Implementation of Hysteresis with Delay
7.2.4 Asymmetric Hysteresis Switch
7.3 Non-Traditional Relay Feedback Methods
7.3.1 Switch Feedback with Integration
7.3.2 Dual-Switch Test
7.3.3 Switch Plus Step
8 Parameter Identification Based on Impulse Response
8.1 Impulse Response Identification
8.1.1 Basic Principles
8.1.2 General Theory
8.1.3 Identification of Simple Model Forms
8.1.4 Obtaining Moments from Experimental Data
8.1.5 Obtaining Impulse Response Data from Other Responses
8.2 Frequency Identification Based on Impulse Response
8.2.1 Frequency Response
8.2.2 Spectrum
8.3 Identification for Self-Regulating Processes
8.4 Simulation Examples
9 Parameter Identification of Multivariable Process Systems
9.1 Basic Concepts of Multivariable System Identification
9.2 TITO Process Closed-Loop Step Tests
9.2.1 Decentralized Identification
9.2.2 Time-Domain Identification
9.2.3 Frequency-Domain Identification
9.3 Identification of General MIMO Processes
9.3.1 Test Process and General Formulas
9.3.2 Decoupled Identification System
9.4 Asymmetric Bilateral Impulse Identification
9.5 Simulation Examples
10 Basic Knowledge of Multivariable System Control
10.1 Basic Concepts
10.1.1 Input/Output Pairing
10.1.2 Mutual Interrelation
10.1.3 Operating Window
10.1.4 Controllability and Observability
10.2 Multivariable Process Models
10.2.1 State-Space Model Form
10.2.2 Transfer Function Model Form
10.2.3 Relationship Between the Two Models
10.3 Open-Loop Analysis
10.3.1 Analytical Solution
10.3.2 Stability
10.3.3 Open-Loop Transfer Function Analysis
10.3.4 Singularity—Singular Values
10.3.5 Dynamic Analysis
10.4 Closed-Loop Dynamic Analysis
10.4.1 Multivariable Block Diagram
10.4.2 Closed-Loop Transfer Function
10.4.3 Closed-Loop Transient Response
10.4.4 Closed-Loop Stability
11 Coupling Analysis of Multivariable Systems
11.1 Preliminary Knowledge
11.1.1 Measure of Control Loop Coupling
11.1.2 Loop Pairing Based on Coupling Analysis
11.2 Relative Gain Array (RGA)
11.2.1 Properties of RGA
11.2.2 Calculation of RGA Based on Principles
11.2.3 Matrix Method for Calculating RGA
11.3 Loop Pairing Using RGA
11.3.1 Explanation of RGA Elements
11.3.2 Basic Pairing Rules
11.4 Additional Rules
11.4.1 Niederlinski Theorem
11.4.2 Niederlinski Pairing Rules
11.4.3 Jacobi Eigenvalue Criterion
11.4.4 Application of Loop Pairing Rules
11.5 Pairing of Other Systems
11.5.1 Loop Pairing for Nonlinear Systems
11.5.2 Loop Pairing for Systems with Integrators
11.5.3 Loop Pairing for Non-Square Systems
11.5.4 Time Decoupling
11.5.5 Loop Pairing Without Process Model
11.6 Relative Interference Gain
12 Decentralized Control of MIMO Processes
12.1 Preliminary Knowledge
12.1.1 General Concepts
12.1.2 Two-Input Two-Output Systems
12.2 Classical Multiloop Controller Design
12.2.1 Designing Multiloop Controllers Using Trial-and-Error Method
12.2.2 Designing Multiloop Controllers Using Optimization Methods
12.2.3 Designing Multiloop Controllers Using RGA Misalignment Factor
12.3 Controller Design Based on Loop Decomposition
12.3.1 Structural Decomposition
12.3.2 Gain and Phase Margin Design
12.3.3 Simulation Examples
12.4 Design Based on Nyquist Stability Criterion
12.4.1 Stability Analysis of Decentralized Control Systems
12.4.2 Stability Domain of Decentralized Systems
12.4.3 Simulation Examples
References