equations of motion, so there is no advance in the state of the art. The coverage of controller design techniques is also very limited in scope. Moreover, apart from the brief tailsquatter chapter, the book does not cover the modeling and control of small fixed-wing aircraft, many of which are illustrated in the photos in Chapter 1. The jacket promises “detailed explanation of the use of the Kalman filter to flying machine localization.”Unfortunately, there is nothing on this subject in the book. Q. Does the book advance the goals of the series? A. The AIC series aims to “report and encourage technology transfer in control engineering.” Several chapters involve no technology transfer (for example, Chapters 1, 7, and 9), and the work described in the other chapters falls short of the state of the art in controlling mini-helicopters. For example, the authors of [5] are part of a team flying a Yamaha R-Max helicopter, which uses a dynamic-inversion controller with neural-network-based adaptation. The teamdesigned sensor electronics include a GPS-aided, low-cost inertial navigation system, a three-axis magnetometer, and a sonar altimeter. The helicopter can perform autonomous waypoint navigation and can maneuver aggressively. Q. Is the book well organized and does it cover all of the relevant topics? A. The book is poorly organized and lacking in overall coherence. For example, the chapter “Modelling and Control of Mini-Helicopters” precedes the chapter on the “Quad-Rotor Rotorcraft,” while the earlier chapters on VTOL aircraft involve control of helicopters with mention of UAVs. As a result, I found myself rechecking chapters and topics for duplication. Important control-design techniques and sensors [5] have been omitted. The final chapter on sensors does not focus on mini-flying vehicles and is very poorly written (see, for example, the definition of an INS). An important missing topic is the description of a low-cost integrated (inertial, GPS, altimeter) navigation
Auxiliary Signal Design for Failure Detection By STEPHEN L. CAMPBELL and RAMINE NIKOUKHAH
Princeton University Press, 2004, US$42.00 ISBN: 0-691-09987-1
With the development of advanced smart structures and their use in increasingly harsher environments, there has been significant interest in damage detection for structural health monitoring. This interest is
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system with a Kalman filter used to combine the various sensor measurements. Q. Is this book suitable for use as a textbook? A. The very narrow focus of the book makes it a poor choice for a class textbook. Q. Is the book useful for researchers? A. Again, the answer is no, based on the second comment above. In summary, this book is uneven in style, lacks coherence, and is restricted in its coverage of topics. There seems to be a lack of editorial control exerted by the AIC series editors, particularly in the case of the last chapter. The book also fails to convey a sense of the wide interest in UAVs as well as the variety of control techniques currently being investigated by researchers, for example, nonlinear and adaptive control, neural networks, vision-based control, and four-dimensional navigation. Brian L. Stevens
REVIEWER INFORMATION Brian L. Stevens obtained a Ph.D. in electrical engineering from Manchester University, United Kingdom, in 1966. He is retired from Georgia Tech Research Institute and Georgia Tech School of Aerospace Engineering and is presently consulting in the flight controls area. He is a coauthor with Frank L. Lewis of Aircraft Simulation and Control, 2nd ed. (Wiley, 2003).
REFERENCES [1] UAV forum [Online]. Available: http://uavforum.com [2] Entomopter Project [Online]. Available: http://avdil.gtri. gatech.edu/RCM/RCM/Entomopter/ EntomopterProject.html [3] T.J. Meuller, Fixed and Flapping Wing Aerodynamics for Micro Air Vehicle Applications. Washington, DC: AIAA, 2001. [4] Dickinson Lab [Online]. Available: http://dickinson.caltech.edu/ old/people_dickinson.html [5] E.N. Johnson and S.K. Kannan, “Adaptive trajectory control for autonomous helicopters,” AIAA J. Guid., Control,. Dyn., vol. 28, no. 3, 2005.
demonstrated by several dedicated journals. Auxiliary Signal Design for Failure Detection tackles this problem in a more general context by defining failure detection as the determination of changes in a generic system and deciding whether these changes are associated with parameter variations indicative of failure. Here, the systems of interest include not only structures but also complex devices or processes. In addition, quantifying the level of failure is also of interest, and it is accomplished by means of failure identification. The authors discuss not only passive approaches, which are the most common, but also active approaches. In the passive approaches to failure detection, the dynamics (input-output relations) of the monitored system are observed and changes are correlated with the presence and
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type of failure. In contrast, active approaches are based on the injection of an auxiliary signal that is designed to interrogate the monitored system by providing the opportunity for the system to exhibit abnormal behavior when failures are present. The auxiliary signals are computed offline and injected as an open-loop control during certain time intervals referred to as detection intervals. The book focuses on a general framework in which the detection interval is short and requires careful accounting for the initial conditions. In that framework, the main objective is to develop a methodology for constructing optimal auxiliary signals for use in a multimodel approach in conjunction with adequately designed observers. The unique approach to this main goal complements the large body of literature that discusses this field, and the authors graciously acknowledge several books that provide detailed reviews.
CONTENTS The book is well organized and includes an insightful yet concise introduction (Chapter 1), followed by three main chapters (Chapters 2, 3, and 4) and two concluding ones (Chapters 5 and 6). In Chapter 2, which deals with failure detection, the authors discuss three types of systems of increasing complexity: static, continuous time, and discrete time. Each case is analyzed separately. Three contexts are discussed, namely, when no uncertainty is present, when additive noise is included, and when model uncertainty is accounted for. This chapter also discusses issues related to real-time implementations of the methodologies presented. In certain cases, for example, the detection interval is required to be small so that failure does not occur during that interval; that is, failure occurs either before or after the detection interval. Finally, this chapter summarizes several results that are used in many of the detection problems discussed. These detection problems are often reduced to solving optimization problems and, hence, the authors discuss briefly static, dynamic, and discrete-time optimization issues. The third chapter discusses multimodel formulations. The key idea behind the use of multimodels is that failure identification for linear systems may be cast in the form of a multimodel identification process. Hence, failure detection is based on two models, one of which is the original/operational system, while the other is the system with a failure. This chapter discusses auxiliary signals that can be used for guaranteed failure detection, referred to as proper signals. In particular, the focus is on the construction of optimal proper auxiliary signals and online detection tests for the static, continuous-time, and discrete-time systems, with a focus on two-model cases. In addition, several numerical issues related to the implementation of the presented algorithms are discussed. The case of online measured inputs as well as the use of more general cost functions are investigated. Most of the discussion in the first part of this chapter is focused on the case in which the energy of the auxiliary signal is mini-
mized, so that the system is largely unaffected by the test. In certain applications, however, other more stringent requirements may be needed. For example, the system may be required to be at the zero state at the end of the detection interval. Such requirements are accounted for by casting the failure detection problem as an optimization problem with more general cost functions. Furthermore, particular attention is paid to online applications, which demand that computations be as fast as possible and, hence, a premium is placed on the computation speed and the simplicity of the numerical solution technique. The most physically intuitive example discussed is presented in this chapter. This example discusses the dynamics of a suspension-like system, where a mass is attached to a rolling wheel through a spring-mass system, and the control is applied to the wheel in the form of a torque. While this example is simple (ignoring friction, for example), it is a good venue for a clear demonstration of the various approaches. A model is easily developed for this suspension-like system. The additive noise formulation is presented, followed by a discussion of model uncertainty, which corresponds to uncertainty in the gain of the control torque. A more general cost function is then introduced to account for cases in which the suspension is required to return the system close to its original steady state behavior at the end of the detection interval. While the practical context in which a suspension would have such requirements is not discussed, the effectiveness of these approaches is clearly demonstrated. Toward its end, this chapter includes a short discussion of the asymptotic behavior of the system when the duration of the detection interval tends to infinity, along with a summary of useful results used in this chapter and the next. Chapter 4 focuses on direct optimization formulations. Distinct from the approaches employed in the previous chapters, here the auxiliary signal design problem is cast directly in the form of an optimization problem. In particular, the focus is primarily on additive noise, while an optimization formulation is discussed for a two-model case first and a more general m-model case next. Several examples of the two-model case are presented. Also, the possibility of early detection of failures is discussed, where early detection represents detection before the test interval is over. Several important extensions are discussed as well. For example, a few of the effects of nonlinearities are discussed, and a more in-depth analysis of systems with delays is presented, including several computational examples. Finally, setting error bounds and accounting for model uncertainty are briefly discussed at the end of this chapter. An insightful discussion of open questions and extensions is also included. In particular, the authors focus on alternate optimization goals, such as requiring that the time interval for perfect identification be the shortest possible when the noise measure and the auxiliary signal cost are given. Also, the construction of uncertainty models is discussed by considering the problem of how to choose the
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weights that characterize the relative effects of the noise bounds and the measure of the auxiliary signals. Very importantly, issues related to large-amplitude nonlinear dynamics and the need for feedback approaches are underlined. Also, computational issues related to online applications are discussed along with hybrid and sampled-data systems and stochastic modeling issues such as auxiliary signal design for hybrid stochastic-deterministic systems. The last chapter includes useful programs provided to demonstrate several of the approaches presented. These programs are available in the MATLAB-like software package Scilab, which is freely available. This chapter adds to the set of practical tools provided in the text and complements the summaries of useful results at the end of most chapters, which are particularly efficient. This last section also supports the examples included in the text, which are in a large measure designed to demonstrate the concepts being presented and less concerned with particular practical applications and their details.
CONCLUSIONS Aimed at a broad audience that includes graduate students in engineering and applied mathematics, the book is notable for its emphasis and focus on mathematical intuition and numerical issues. It is very well written, with attention to detail and rigor and yet without cluttering the text with
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PERSPECTIVES
overly pedantic material. While aimed primarily at applied mathematicians and engineers with a background in control, the material is accessible to a wide audience with interests in areas such as control theory, functional analysis, optimization, and theory of differential equations. Examples are provided in many places throughout the text, although exercise problems are not included, which may play a role in deciding whether to use this book as a textbook for a graduate or advanced undergraduate course. Bogdan I. Epureanu
REVIEWER INFORMATION Bogdan I. Epureanu (
[email protected]) received his Ph.D. in mechanical engineering from Duke University in 1999. In 2002, he joined the Department of Mechanical Engineering at the University of Michigan, where he is an assistant professor. His interests include nonlinear dynamics, structural health monitoring, sensors, reduced order modeling of fluid-structural systems (aeroelasticity, unsteady aerodynamics), and control of nonlinear systems. He received the 2004 American Academy of Mechanics Junior Achievement Award, an NSF CAREER Award, the 2003 ASME/Pi Tau Sigma Gold Medal Award, the 1998 A.M. Strickland Prize of the Institution of Mechanical Engineers, and the 2004 Beer and Johnston Outstanding Mechanics Educator Award of the ASEE Mechanics Division.
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CONCLUSIONS
AUTHOR INFORMATION
Despite the superiority of the Nyquist criterion, Küpfmüller’s work should not be underestimated. His generic systems approach was novel and informed much of the later work in this area, mediated (at least for the Englishspeaking world) by writers such as Guillemin. In Germanspeaking areas, Küpfmüller is considered to be a major figure of 20th century communications and information engineering. An obituary [8] in 1977 puts it as follows: “With the death of Karl Küpfmüller we have lost one of the fathers of modern communication theory . . . If, today, we recognize information along with energy and matter as a third fundamental building block of the world, then Karl Küpfmüller has been a major contributor to the recognition of this fact.”
Chris Bissell (
[email protected]) is professor of telematics at the United Kingdom Open University, where he has contributed to distance learning courses in control engineering, signal processing, telecommunications, and media studies. He has researched the history of technology for many years, concentrating on control and telecommunications in Germany and Russia.
A NOTE ON THE TRANSLATION In the full online translation [5], I use English terminology that would have been contemporary with the original publication. When Küpfmüller used terms in German that were (as far as I am aware) later superseded, I have opted for a literal translation, rather than give a modern English equivalent. Readers are invited to send comments or corrections to c.c.bissell@ open.ac.uk.
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REFERENCES [1] K. Küpfmüller, Einführung in die theoretische Elektrotechnik [Introduction to the theory of electrical engineering]. Berlin: Julius Springer, 1932. [2] K. Küpfmüller, Theoretische Elektrotechnik: Eine Einführung [Theory of electrical engineering: An introduction], 17th ed. (revised and extended by W. Mathis and A. Reibiger). New York: Springer-Verlag, 2006. [3] K. Küpfmüller, “Über die Dynamik der selbsttätigen Verstärkungsregler [On the dynamics of automatic gain controllers],” Elektrische Nachrichtentechnik, vol. 5, no. 11, pp. 459–467, 1928. [4] C.C. Bissell, “Karl Küpfmüller: A German contributor to the early development of linear systems theory,” Int. J. Contr., vol. 44, no. 4, pp. 977–989, 1986. [5] Classic papers in information and communication technology [Online]. Available: http://ict.open.ac.uk/classics [6] K. Küpfmüller, “Über Einschwingvorgänge in Wellenfiltern [On transient processes in wave filters],” Elektrische Nachrichtentechnik, vol. 1, no. 5, pp. 141–152, 1924. [7] E. Guillemin, Communication Networks, vol. 2. New York: Wiley, 1935. [8] A. Vlcek, “Karl Küpfmüller [an obituary],” Nachrichten Elektronik, vol. 32, p. 38, no. 8, 1978.
