Process Control Design for Industrial Applications

Dumitru Popescu, Amira Gharbi, Dan Stefanoiu, Pierre Borne

• Publisher: Wiley-ISTE
• Year: 2017
• Print ISBN: 9781786300140
• eBook ISBN: 9781119407935
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Quality Control in Engineering
• Index: Technology & Engineering

This book presents the most important methods used for the design of digital controls implemented in industrial applications. The best modelling and identification techniques for dynamical systems are presented as well as the algorithms for the implementation of the modern solutions of process control. The proposed described methods are illustrated by various case studies for the main industrial sectors

There exist a number of books related each one to a single type of control, yet usually without comparisons for various industrial sectors. Some other books present modelling and identification methods or signal processing. This book presents the methodsto solve all the problems linked to the design of a process control without the need to find additional information.

1
Introduction – Models and Dynamic Systems

This chapter is a presentation of the issues surrounding the design and implementation of automatic control solutions for industrial applications. Currently, there is an increasing demand for setups that are productive and offer a good yield for the provider by means of modern concepts such as “embedded optimization” and “low-cost automation”. In this context, Automation and Applied Informatics, through their theoretical tools, as well as hardware and software resources, contribute toward the development of applications using the existing technologies as well as the emergent ones. Automation makes it possible to describe the evolution of a real process using an abstract mathematical model, and to introduce the necessary methods and mechanisms to design the advanced control systems that ensure an optimal operation of the process. On the other hand, Informatics, due to the hardware material and software tools available and the efficiency of its powerful computing and communication which contribute toward operations involving data processing, model development, design and identification of control methods, is vital for the optimization, surveillance and protection of industrial processes. The different classes of dynamic models (systems) associated with the processes, as well as the most important algorithms, methods and techniques used in the design of the control systems for industrial facilities, are briefly presented here.

1.1. Overview

The design and implementation of digital control systems for industrial applications has, as a primary objective, the valorization of Automatic Control and Informatics tools in order to improve the quality of products, to ensure the safety of personnel, protect industrial facilities and the environment, and decrease as much as possible the production costs.

This objective is a response to the increasing demands in efficiency and autonomous operating requirements of companies, the need to reduce the role of the operator during process use (facilities, machines and equipment start to communicate with each other using the mechanisms involved in the concept of the internet of things) and the augmenting integration of automation into high-performance production technologies.

Two main categories of Industrial Automatic Control that are required are as follows: first, the mathematical formulism that allows the process to be represented by a mathematical model that expresses its dynamics, and second, the concepts and methods used in the control and optimization of the process [OGA 90, KUO 91, AST 97, GEN 97, POP 06].

A mathematical model is set using the laws that govern the functioning of the process or by using a collection of data acquired from the process.

The control algorithms are calculated based on the model, using numerical methods so as to ensure the level of performance desired in terms of the real processes. In the following step, which has become mandatory, an optimal operating point is found in the real operating conditions using an adequate decision criterion fo...

Table of contents

• Cover
• Table of Contents
• Title
• Copyright
• Preface
• List of Acronyms and Notations
• 1 Introduction – Models and Dynamic Systems
• 2 Linear Identification of Closed-Loop Systems
• 3 Digital Control Design Using Pole Placement
• 4 Adaptive Control and Robust Control
• 5 Multimodel Control
• 6 Ill-Defined and/or Uncertain Systems
• 7 Modeling and Control of an Elementary Industrial Process
• 8 Industrial Applications – Case Studies
• Appendix A: Matrix Transformation from Any Representation to the Companion Form or Arrow Form
• Appendix B: Determination of the Maximum Error for Pole Placement for a Nonlinear Third-Order Process
• Appendix C: Determining the Attractor in a Nonlinear Process Controlled by Linear Decoupling
• Appendix D: Overvaluation of the Maximum Error in a Tracking Problem for a Lur’e Postnikov Type Process
• Blibliography
• Index
• End User License Agreement