Industrial PID Controller Tuning: With a Multiobjective Framework Using MATLAB® (Advances in Industrial Control)

Series Editor’s Foreword
Acknowledgements
Contents
Abbreviations and Symbols
	Abbreviations
	Symbols
1 Introduction
	References
2 Industrial PID Control
	2.1 Control System Design Scenario
	2.2 Industrial Process Characteristics
		2.2.1 Controlled Process Model
	2.3 The PID Controller
		2.3.1 Proportional Term
		2.3.2 Integral Term
		2.3.3 Derivative Term
		2.3.4 PID Controller Formulations
		2.3.5 Reference Processing and 2DoF PID
		2.3.6 Conversion of 2DoF PID Controller Algorithms
	2.4 Normalized Representations
		2.4.1 Process Model Normalization
		2.4.2 Controller Normalization
	References
3 PID Controller Considerations
	3.1 Control System Evaluation Metrics
		3.1.1 Performance
		3.1.2 Robustness
		3.1.3 Control Input Usage
	3.2 Control System Trade-Offs
		3.2.1 Servo Versus Regulation
		3.2.2 Performance Versus Robustness
		3.2.3 Input Versus Output Disturbances
	References
4 PID Controller Design
	4.1 PID Controller Tuning
		4.1.1 Analytical Tuning Methods
		4.1.2 Tuning Based on Minimization of Performance Criteria
		4.1.3 Tuning Rules for Robustness
	4.2 Formalization of PID Tuning as a Multiobjective Optimization Problem
		4.2.1 Cost Function and Constraint Selection
		4.2.2 PID Tuning Problem Formulation for Integral Cost Functions
	References
5 Multiobjective Optimization
	5.1 Formalization of the Multiobjective Optimization Problem
		5.1.1 Definition of the Pareto Front
		5.1.2 Practical Example
		5.1.3 Different Approaches to Obtain the Pareto Front
	5.2 Scalarization Algorithms to Find the Pareto Front
		5.2.1 Weighted Sum
		5.2.2 Normal Boundary Intersection
		5.2.3 Normalized Normal Constraint
		5.2.4 Enhanced Normalized Normal Constraint
	5.3 Solution Selection from the Pareto Front
		5.3.1 Visualization of the Pareto Front
		5.3.2 Pareto as a Decision Model
		5.3.3 Pareto Front as Part of a Decision Tool
	References
6 Application of the Multiobjective Approach
	6.1 Comparison of the Methods to Obtain the Pareto Front
		6.1.1 Performance Comparison of the Scalarization Methods
		6.1.2 Analysis of the Results from the Control Theory Perspective
	6.2 High-Order Benchmark Plant
	6.3 LiTaO3 Thin Film Deposition Process
	References
7 PID Tuning as a Multiobjective Optimization Problem
	7.1 Solution of the Multiobjective Optimization Tuning
	7.2 Viability for Tuning Rules
		7.2.1 Tuning of a PI Controller with Two Objectives
		7.2.2 Tuning for a Three-Objective PID Controller
		7.2.3 Comments on Creating Tuning Rules from Pareto Fronts
	7.3 Database Approach for the Final Tuning
		7.3.1 Example Using MOOTuning
	References
8 Industrial Application Examples
	8.1 Continuously Stirred Tank Heater
		8.1.1 Description of the Process
		8.1.2 Simplified Linear Model
		8.1.3 PID Control of the CSTH Considering Two Integral Cost Functions
		8.1.4 PID Control of the CSTH Considering Three Integral Cost Functions
	8.2 Continuously Stirred Tank Reactor
		8.2.1 Description of the Process
		8.2.2 Linearization
		8.2.3 Controller Design
		8.2.4 Validation of the Controller Designs
	8.3 Final Remarks
	References