**Meet the editor**

Dr. Mohammad Shamsuzzoha holds a Ph.D. in Chemical Engineering with specialization in process design and control and an MBA in Entrepreneurship and Leadership. He works as a senior process simulation engineering at ADNOC Refining Research Center (ARRC), Abu Dhabi, UAE.

Prior to joining ARRC, he worked at King Fahd Uni-

versity of Petroleum and Minerals, Saudi Arabia, as an assistant professor and at the Norwegian University of Science and Technology Norway as a postdoctoral research fellow. He was intensively involved in research and industrial projects to develop theory and applications in the area of process design, optimization, and control. He has published more than 30 technical papers in refereed chemical engineering journals and around 75 papers in proceedings in international conferences.

Contents

**Preface VII**

Jyh-Cheng Jeng

**Algorithm 43**

**Performance 73**

Chapter 6 **Distillation Column 133**

Nasser Mohamed Ramli

**Analytical Design 147**

and Nicolás Muñoz-Galeano

Štefan Bucz and Alena Kozáková

Chapter 5 **PID Control for Takagi-Sugeno Fuzzy Model 121** Taieb Adel and Chaari Abdelkader

Chapter 7 **Constraint Handling Optimal PI Controller Design for**

Rodrigue Tchamna and Moonyong Lee

**Integrating Processes: Optimization-Based Approach for**

Chapter 1 **Data-Based Tuning of PID Controllers: A Combined Model-**

Chapter 2 **Maximum Peak-Gain Margin (Mp-GM) Tuning Method for Two**

Juwari Purwo Sutikno, Nur Hidayah and Renanto Handogo

Jorge-Humberto Urrea-Quintero, Jesús-Antonio Hernández-Riveros

**Reference and VRFT Method 1**

**Degree of Freedom PID Controller 21**

Chapter 3 **Optimum PI/PID Controllers Tuning via an Evolutionary**

Chapter 4 **Advanced Methods of PID Controller Tuning for Specified**

## Contents

### **Preface XI**



Jorge-Humberto Urrea-Quintero, Jesús-Antonio Hernández-Riveros and Nicolás Muñoz-Galeano


### Chapter 8 **Decoupling Control and Soft Sensor Design for an Experimental Platform 167**

Thamiles Rodrigues de Melo, Nathália Arthur Brunet Monteiro, Danilo Pequeno, Jaidilson Jó da Silva and José Sérgio da Rocha Neto

Preface

loop experiments.

signal.

This book is devoted to proportional–integral–derivative (PID) controller theory and its ap‐ plication. PID controllers are probably the most widely used industrial controller in the process industries. They remain important control tools for three reasons: past record of suc‐ cess, wide availability, and simplicity of use. Their stability analysis is extremely easy to car‐ ry out and the design trade-off between performance and robustness is clearly understood. *PID Control for Industrial Processes* presents a clear, multidimensional representation of PID control for both students and specialists working in the area of PID control. It mainly focus‐ es on the theory and application of PID control in industrial processes. It incorporates recent developments in PID control technology in industrial practice. Emphasis has been given to finding the best possible approach to develop a simple and optimal solution for industrial users. This book includes several chapters that cover a broad range of topics and priority has been given to subjects that cover real-world examples and case studies. The book is focused on approaches for controller tuning, i.e., method bases on open-loop plant tests and closed-

Briefly, Chapter 1 presents a novel data-based PID controller tuning method that can be ap‐ plied to stable, integrating, and unstable plants. The tuning method is developed under the virtual reference feedback tuning (VRFT) design framework where the reference model of VRFT is coordinately optimized with the controller on the basis of the model-reference criteri‐ on to ensure the validity of the VRFT approach. Chapter 2 finds the PID setting parameters of two degrees of freedom control structure based on model uncertainty. This tuning method is able to obtain reasonable controller parameters even under process uncertainties on standard two degrees of freedom internal model control. Chapter 3 demonstrates that when using ad‐ vanced evolutionary algorithms, whatever the adopted system model (SOSPD, non-mini‐ mum phase, oscillatory, or non-linear), it is possible to find optimal parameters of PID controllers satisfying simultaneously the behavior of the system and a performance index such as Absolute Integral Error. Multidynamics Algorithm for Global Optimization is used to solve the control problem with PID controllers. Chapter 4 is a concise survey showing the persistent demand for PID tuning algorithms that integrate performance requirements into the tuning algorithm. The proposed frequency-domain PID controller design method guaran‐ tees closed-loop performance in terms of commonly used time-domain specifications. Chap‐ ter 5 emphasizes the problem of controlling the Takagi-Sugeno fuzzy model by PID controllers using particle swarm optimization. A new algorithm is proposed that relies on the use of a new objective function taking into account both the performance indices and the error

### Chapter 9 **PID Controller Design Methods for Multi-Mass Resonance System 187** Hidehiro Ikeda
