Identification of Multivariable Industrial Processes


Book Description

Identification of Multivariable Industrial Processes presents a unified approach to multivariable industrial process identification. It concentrates on industrial processes with reference to model applications. The areas covered are experiment design, model structure selection, parameter estimation as well as error bounds of the transfer function. This publication is intended to fill the gap between modern systems and control theory and industrial application. It is based on the results of 10 years of research and application experiences. The theories and models discussed are fully explained and illustrated with case studies. At an early stage the reader is introduced to real applications.




Multivariable System Identification For Process Control


Book Description

Systems and control theory has experienced significant development in the past few decades. New techniques have emerged which hold enormous potential for industrial applications, and which have therefore also attracted much interest from academic researchers. However, the impact of these developments on the process industries has been limited.The purpose of Multivariable System Identification for Process Control is to bridge the gap between theory and application, and to provide industrial solutions, based on sound scientific theory, to process identification problems. The book is organized in a reader-friendly way, starting with the simplest methods, and then gradually introducing more complex techniques. Thus, the reader is offered clear physical insight without recourse to large amounts of mathematics. Each method is covered in a single chapter or section, and experimental design is explained before any identification algorithms are discussed. The many simulation examples and industrial case studies demonstrate the power and efficiency of process identification, helping to make the theory more applicable. MatlabTM M-files, designed to help the reader to learn identification in a computing environment, are included.




Industrial Process Identification and Control Design


Book Description

Industrial Process Identification and Control Design is devoted to advanced identification and control methods for the operation of continuous-time processes both with and without time delay, in industrial and chemical engineering practice. The simple and practical step- or relay-feedback test is employed when applying the proposed identification techniques, which are classified in terms of common industrial process type: open-loop stable; integrating; and unstable, respectively. Correspondingly, control system design and tuning models that follow are presented for single-input-single-output processes. Furthermore, new two-degree-of-freedom control strategies and cascade control system design methods are explored with reference to independently-improving, set-point tracking and load disturbance rejection. Decoupling, multi-loop, and decentralized control techniques for the operation of multiple-input-multiple-output processes are also detailed. Perfect tracking of a desire output trajectory is realized using iterative learning control in uncertain industrial batch processes. All the proposed methods are presented in an easy-to-follow style, illustrated by examples and practical applications. This book will be valuable for researchers in system identification and control theory, and will also be of interest to graduate control students from process, chemical, and electrical engineering backgrounds and to practising control engineers in the process industry.







Industrial Process Identification


Book Description

Industrial Process Identification brings together the latest advances in perturbation signal design. It describes the approaches to the design process that are relevant to industries. The authors’ discussion of several software packages (Frequency Domain System Identification Toolbox, prs, GALOIS, multilev_new, and Input-Signal-Creator) will allow readers to understand the different designs in industries and begin designing common classes of signals. The authors include two case studies that provide a balance between the theory and practice of these designs: the identification of a direction-dependent electronic nose system; and the identification of a multivariable cooling system with time-varying delay. Major aspects of signal design such as the formulation of suitable specifications in the face of practical constraints, the classes of designs available, the various objectives necessitating separate treatments when dealing with nonlinear systems, and extension to multi-input scenarios, are discussed. Codes, including some that will produce simulated data, are included to help readers replicate the results described. Industrial Process Identification is a powerful source of information for control engineers working in the process and communications industries seeking guidance on choosing identification software tools for use in practical experiments and case studies. The book will also be of interest to academic researchers and students working in electrical, mechanical and communications engineering and the application of perturbation signal design. Advances in Industrial Control reports and encourages the transfer of technology in control engineering. The rapid development of control technology has an impact on all areas of the control discipline. The series offers an opportunity for researchers to present an extended exposition of new work in all aspects of industrial control.







Control Configuration Selection for Multivariable Plants


Book Description

Control of multivariable industrial plants and processes has been a challenging and fascinating task for researchers in this field. The analysis and design methodologies for multivariable plants can be categorized as centralized and decentralized design strategies. Despite the remarkable theoretical achievements in centralized multiva- able control, decentralized control is still widely used in many industrial plants. This trend in the beginning of the third millennium is still there and it will be with us for the foreseeable future. This is mainly because of the easy implementation, main- nance, tuning, and robust behavior in the face of fault and model uncertainties, which is reported with the vast number of running decentralized controllers in the industry. The main steps involved in employing decentralized controllers can be summarized as follows: • Control objectives formulation and plant modeling. • Control structure selection. • Controller design. • Simulation or pilot plant experiments and Implementation. Nearly all the textbooks on multivariable control theory deal only with the control system analysis and design. The important concept of control structure selection which is a key prerequisite for a successful industrial control strategy is almost unnoticed. Structure selection involves the following two main steps: • Inputs and outputs selection. • Control configuration selection or the input-output pairing problem. This book focuses on control configuration selection or the input-output pairing problem, which is defined as the procedure of selecting the appropriate input and output pair for the design of SISO (or block) controllers.




System Identification (SYSID '03)


Book Description

The scope of the symposium covers all major aspects of system identification, experimental modelling, signal processing and adaptive control, ranging from theoretical, methodological and scientific developments to a large variety of (engineering) application areas. It is the intention of the organizers to promote SYSID 2003 as a meeting place where scientists and engineers from several research communities can meet to discuss issues related to these areas. Relevant topics for the symposium program include: Identification of linear and multivariable systems, identification of nonlinear systems, including neural networks, identification of hybrid and distributed systems, Identification for control, experimental modelling in process control, vibration and modal analysis, model validation, monitoring and fault detection, signal processing and communication, parameter estimation and inverse modelling, statistical analysis and uncertainty bounding, adaptive control and data-based controller tuning, learning, data mining and Bayesian approaches, sequential Monte Carlo methods, including particle filtering, applications in process control systems, motion control systems, robotics, aerospace systems, bioengineering and medical systems, physical measurement systems, automotive systems, econometrics, transportation and communication systems *Provides the latest research on System Identification *Contains contributions written by experts in the field *Part of the IFAC Proceedings Series which provides a comprehensive overview of the major topics in control engineering.




Multivariable Control Systems


Book Description

Multivariable Control Systems focuses on control design with continual references to the practical aspects of implementation. While the concepts of multivariable control are justified, the book emphasises the need to maintain student interest and motivation over exhaustive mathematical proof. Tools of analysis and representation are always developed as methods for achieving a final control system design and evaluation. Features: • design implementation laid out using extensive reference to MATLAB®; • combined consideration of systems (plant) and signals (mainly disturbances); • step-by-step approach from the objectives of multivariable control to the solution of complete design problems. Multivariable Control Systems is an ideal text for graduate students or for final-year undergraduates looking for more depth than provided by introductory textbooks. It will also interest the control engineer practising in industry and seeking to implement robust or multivariable control solutions to plant problems.




Advanced Process Identification and Control


Book Description

A presentation of techniques in advanced process modelling, identification, prediction, and parameter estimation for the implementation and analysis of industrial systems. The authors cover applications for the identification of linear and non-linear systems, the design of generalized predictive controllers (GPCs), and the control of multivariable systems.