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Industrial Process Identification and Control Design: Step-Test and RelayExperiment-Based Methods by Tao Liu and Furong Gao Reviewed by Pedro Albertos
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any books and monographs about industrial process control design describe their reliance on process models. Many books and monographs on modelAdvances in Industrial Control, Springer-Verlag, ing and identification describe London, United Kingdom, different approaches to build 2012, ISBN: process models based on first 978-0-857299765, principles, experimentation, or 472 pages, US$229. some combination of the two. However, the models needed for control system design are very different from the models needed for the design of the process units. The fields of process control and system identification are very wide areas with plenty of interesting problems, but in practice “industrial process identification and control design” requires simple approaches that provide solutions applicable to a large class of situations. The main goal of this monograph is to deal with both problems in an integral way: given a process to be controlled, how should a simple model be identified and used to design a controller to fulfill some requirements? This book is neither a comprehensive handbook of process control design [1] nor a treatise on system identification and parameter estimation [2] but rather provides the tools to reach the final objective of designing a control system for an industrial process.
Digital Object Identifier 10.1109/MCS.2013.2295727 Date of publication: 14 March 2014
78 IEEE CONTROL SYSTEMS MAGAZINE » april 2014
Contents of the Book The book has two parts: Part I: Process Identification (Chapters 1–6) and Part II: Control System Design (Chapters 7–12). Part I provides a basis for applying the control methods presented in Part II. Both parts are self-contained and could be read independently according to the needs of the readers. Most of the book is mathematically rigorous (see the later comment on Chapter 11), and good physical insight is provided, with many examples taken from the literature and used to compare the techniques. The widely used first-order-plus-dead-time (FOPDT) and second-order-plus-dead-time (SOPDT) model structures are chosen for identification. In this way, only a few parameters are needed to describe the dynamics of a large proportion of industrial processes. For the sake of completeness, in some cases the results are extended to higher order models with time delays. Step responses [3] and the use of relay in the loop to generate sustained oscillations [4], [5] have been extensively used in practice to obtain simple models. These methods are used in the book to identify the reduced-order model of the plant. The use of a step test for model identification has a longer history than the relay test, as can be read in the early survey [6], but the relay test has the advantage of being implemented in closed loop. One of the main difficulties in handling experimental data is the presence of measurement noise. Generally, experimental data are filtered and integrated to get a smoother set of data. Different identification algorithms are used according to the selected model structure, and guidelines to choose the involved parameters are suggested and discussed. A number of examples illustrate the practical use of these algorithms. In the second part of the book, these reduced-order models are used to design a variety of control structures, including single loop control, two-degree-of-freedom (2DOF) controllers, cascade control, multiloop control, decoupling control, and batch control. The main issues considered in the design are disturbances, noise rejection, robustness, and setpoint tracking, with special attention paid to the control of multivariable time-delayed plants. The content of the book is well structured, and each chapter deals with a specific topic. The scope and objective of process identification, the excitation signals commonly used for open-loop and closed-loop identification tests, and the model fitting criteria are summarized in Chapter 1. 1066-033X/14/$31.00©2014ieee
The next two chapters deal with identification methods for open-loop stable and unstable processes using an open- or closed-loop step test. A frequency response estimation method is introduced and applied. The model structures chosen for identification are simple models with a time delay. The initial identification algorithms are modified to handle data obtained from experiments with nonzero initial conditions and/or external disturbances. Several examples illustrate the use of the algorithms, and, by the experimental application to a screw injection molding machine, additional insight in the procedure is provided. The use of simple control structures based on the obtained model combining feedforward, 2DOF control, and internal model control compensation enters into the identification/control-design integral problem. An application to the startup heating control of barrel temperature for an industrial injection molding machine is reported, and practical implementation issues are discussed. The next three chapters are devoted to closed-loop identification methods by using a relay feedback test. The implementation of a relay test of biased or unbiased type is briefly introduced, followed by the guidelines for model structure selection and a list of different relay response shapes for reference. Analytical relay response expressions are derived for FOPDT and SOPDT models, and the corresponding model identification algorithms are summarized. Furthermore, by considering the stable limit-cycle oscillation and based on a frequency-response estimation algorithm, a generalized relay identification method for obtaining a model of any order with time delay is presented. The general approaches are introduced in Chapter 4, dealing with stable processes, and the particularities to be applied to integrating and unstable processes are discussed in Chapters 5 and 6, respectively. Several examples are developed to illustrate the applications of the different algorithms and an application to an industrial injection molding machine is also reported. Part II of the book is concerned with model-based control design. The process models considered are simple and with time delay, as covered in the previous chapters. An introduction of control engineering specifications in both the time and frequency domains, along with closed-loop robust stability criteria, is reviewed in Chapter 7. Internal model control (IMC) design, with proportional-integral-derivative (PID) controllers, is also reviewed, and tuning formulas are provided for the case of single-input, single-output control. Special attention is given to disturbance rejection. Chapter 8 describes 2DOF control methods to solve the problems of setpoint tracking and load disturbance rejection that are suitable for stable, integrating, and unstable processes. Several benchmark examples are included to illustrate the design methodology, improvements with respect to single-loop control, and robustness under process model uncertainties. The closed-loop performance can be improved with cascade control, as presented in Chapter 9, which is particularly interesting for unstable processes, where two distur-
bance rejection controllers are included, leading to a 3DOF control scheme. Several examples taken from the literature illustrate the advantages of these control structures. Multiple-input, multiple-output (MIMO) processes are treated in Chapters 10 and 11. The complexity of the design and the resulting controllers are discussed first and, with a practical perspective, the PID parameter approximations are introduced. Decoupling control methods for MIMO processes, both in the framework of the unity feedback control structure and by using a 2DOF control scheme, are outlined but a more complete study would be appreciated. Again, several examples taken from the literature illustrate the control design procedure. An introduction to batch process control and the specific implementation requirements are presented in Chapter 13. For this kind of application, the iterative learning control (ILC) paradigm is very appropriate, and an IMC-based ILC scheme for trajectory tracking in the presence of process response delay and time-varying uncertainties is proposed. Some concluding remarks summarizing the main contributions of the book are collected in Chapter 13, pointing out the many open issues remaining in these challenging fields.
Conclusions This book is interesting for people seeking an introduction into experimental modeling and process control design. The authors develop some theoretical techniques to both ends, but mainly pay attention to the practical issues to implement algorithms for different scenarios: the identification approach (by considering the step response or relay oscillations analysis) and the model structure on one hand and by considering different control structures on the other hand. Some minor details would improve the quality of the monograph. Some parts seem to come from different sources, so different notations are used. For instance, the general SOPDT model uses different parameters and notation in (2–27), (4–5), and (7–67). Intended as a book for graduates and practitioners, the models derived in Part I, which use too many significant figures, are not clearly connected to the models in Part II, which use rough approximations. The applications of the identification algorithms in Part I lead to numerically computed models where the high-precision parameters are senseless, when obtained from the raw and filtered data. The book would be more convenient if it included a discussion about the effect of rounding the coefficients, as a matter of practical robustness of the models and the controllers. Other than the multiple exercises, some additional applications dealing with industrial plants would provide better insight into practical and implementation issues. Some assertions are doubtful (too strong), and some notation is misused. On page 335, derivatives and difference equations appear together. Also, the book is restricted to linear models and linear control, and any reference to nonlinear models should be avoided or properly treated. april 2014 « IEEE CONTROL SYSTEMS MAGAZINE 79
The presentation style of ”method + illustrative example(s)” is really easy to follow by readers from academia or industry. The brief summary at the end of each chapter could be expanded with a condensed survey on the research literature to motivate future development in the research community. As a textbook in graduate courses, it would be helpful to have an exercise section along with answers or hints at the end of each chapter. Also, it would be nice to provide some appendices, including the fundamental mathematical knowledge and tools needed for understanding the theoretical analyses associated with the proposed methods.
Reviewer Information Pedro Albertos (
[email protected]) has been a full professor since 1975 at the Systems Engineering and Control Department at the Univesitat Politècnica Valencia, Spain, and was director from 1979 to 1995 and in 1998. He has been teaching courses on advanced control systems, intelligent control systems, and systems theory. He is an honorary professor at Northeastern University, Shenyang, China, and doctor honoris causa at the Universities of Oulu, Finland, and Polytechnic of Bucharest, Romania. As an invited professor, he has delivered courses and seminars at more than 30 universities and research centers. He has coauthored more than 300 papers, book chapters, and conference communications; the books Multivariable Con-
trol Systems (Springer, 2004) and Feedback and Control for Everyone (Springer, 2010); and has coedited seven books. He has directed 18 Ph.D. theses; is the coordinator of the Ph.D. program on automation and industrial informatics, which has been implemented in Spain, Mexico, Columbia, and Venezuela; is involved in many national and international research projects; and currently participates in the Prometeo research project of excellence in Ecuador. He is an associate editor of Control Engineering Practice and editor-in-chief of the journal RIAI (Revista Iberoamericana de Automática e Informática Industrial). He is a Fellow of the International Federation of Automatic Control (IFAC), an IFAC advisor, and a Senior Member of IEEE. He was IFAC president (1999–2002) and a member of the Board of Governors of the IEEE Control Systems Society (1996–1997).
References [1] W. S. Levine Ed., The Control Handbook. Boca Raton, FL: CRC Press, 1996. [2] L. Ljung, System Identification: Theory for the User, 2nd ed. Englewood Cliff, NJ: Prentice Hall, 1999. [3] V. Strejc, “Approximate determination of the control characteristics of an aperiodic response process,” Automatisme, vol. 5, Apr. 1960. [4] D. P. Atherton, “Oscillations in relay systems,” Trans. Inst. Meas. Control, vol. 3, no. 4, pp. 171–184, Oct. 1981. [5] K. J. Åström and T. Hägglund, “Automatic tuning of simple regulators with specification on phase angle and amplitude margins,” Automatica, vol. 20, no. 5, pp. 645–651, 1984. [6] H. Rake, “Step response and frequency response methods,” Automatica, vol. 16, no. 5, pp. 519–526, 1980.
Book Announcements Control and Optimization with PDE Constraints by Kristian Bredies, Christian Clason, Karl Kunisch, and Gregory von Winckel, Editors
Many mathematical models of physical, biological, and social systems involve partial differential equations (PDEs). The desire to understand and influence these systems naturally Birkhäuser, 2013, ISBN: leads to considering problems 978-3-0348-0630-5, of control and optimization. 218 pages, US$109.00. This book presents topics in the areas of control of PDEs and of PDE-constrained optimization, covering the spectrum from analysis to numerical
Digital Object Identifier 10.1109/MCS.2013.2295728 Date of publication: 14 March 2014
80 IEEE CONTROL SYSTEMS MAGAZINE » april 2014
realization and applications. Leading researchers address current topics such as nonsmooth optimization, Hamilton– Jacobi–Bellman equations, issues in optimization and control of stochastic partial differential equations, reducedorder models and domain decomposition, discretization error estimates for optimal control problems, and control of quantum-dynamical systems. These contributions all originate from the International Workshop on Control and Optimization of PDEs held in October 2011. This book is appropriate for students and researchers in the area of control and optimization of differential equations. Readers interested the development of numerical algorithms will also find this book of interest.
Control of Noise and Structural Vibration: A MATLAB-Based Approach by Qibo Mao and Stanislaw Pietrzko
This book presents a Matlab-based approach to solving the problems of 1) undesirable noise generation and transmission by structures and 2) undesirable vibration within structures
