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P. A. Cook

Bio: P. A. Cook is an academic researcher. The author has an hindex of 1, co-authored 1 publications receiving 6 citations.

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TL;DR: In this paper, the authors present a book on multivariable system theory and design for postgraduate students with a focus on pole assignment, frequency domain, design techniques and robust servomechanism problem.
Abstract: Multivariable System Theory and Design: RAJNIKANT V. PATEL and NEIL MUNRO (Pergamon Press, 1982, 374 pp., £19.50 hardback, £9.50 paperback) There are some books whose presentation and content one immediately takes a liking to, and this book falls into that category. The book seems very appropriate for use by M.Sc. and Ph.D. students and it is also very up-to-date. I particularly liked the chapters on poles and zeros of multivariable systems, pole assignment, frequency domain, design techniques and the robust servomechanism problem. It is also nice to see the method of inequalities due to Zakian included in a student text (for the first time, I believe).The book contains an extensive list of references which should be helpful to research students. The subject is very mathematical but the treatment does not rely on advanced mathematics. The book should be of value to control engineers working in industry, but it does not address itself to the practical aspects of engineering problems. However, the description of the INA design technique which was developed at UMIST and is readily available on CAD computer packages will be valuable to engineers. The characteristic locus frequency domain design method (developed at Cambridge) which has also found wide application is described and examples given. It is a credit to the authors that most of the modern multi variable design techniques are considered and not only those that they have developed. I would have preferred more material on optimal control and a chapter on Kalman filtering but there is, of course, a limit on the size of such a text. In all, the book is well balanced and should be high on the list of recommended texts for postgraduate students. M. J. GRIMBLE, Professor ofElectrical Engineering. University ofStrathclyde. Glasgow

6 citations


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TL;DR: A set of sufficient conditions is developed in terms of controllability and observability functions under which a given state-space realization of a formal power series is minimal.
Abstract: In this paper a set of sufficient conditions is developed in terms of controllability and observability functions under which a given state-space realization of a formal power series is minimal. Specifically, it is shown that positivity of these functions, in addition to a stability requirement and a few technical conditions, implies minimality. Using the nonlinear analogue of the Kalman decomposition, connections are then established between minimality, singular value functions, balanced realizations, and various notions of reachability and observability for nonlinear systems.

70 citations

Journal ArticleDOI
TL;DR: In this paper, a reduced-order-modeling approach for nonlinear, multi-degree-of-freedom aerodynamic systems using multi-input Volterra theory is presented.
Abstract: This paper presents a reduced-order-modeling approach for nonlinear, multi-degree-of-freedom aerodynamic systems using multi-input Volterra theory. The method is applied to a two-dimensional, 2 degree-of-freedom transonic airfoil undergoing simultaneous forced pitch and heave harmonic oscillations. The so-called Volterra cross kernels are identified and shown to successfully model the aerodynamic nonlinearities associated with the simultaneous pitch and heave motions. The improvements in accuracy over previous approaches that effectively ignored the cross kernels by using superposition are demonstrated.

65 citations

Journal ArticleDOI
TL;DR: This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools, and gives a basic tutorial on tensors, and suggests further open EDA problems where the use of tensor computation could be of advantage.
Abstract: Many critical electronic design automation (EDA) problems suffer from the curse of dimensionality, i.e., the very fast-scaling computational burden produced by large number of parameters and/or unknown variables. This phenomenon may be caused by multiple spatial or temporal factors (e.g., 3-D field solvers discretizations and multirate circuit simulation), nonlinearity of devices and circuits, large number of design or optimization parameters (e.g., full-chip routing/placement and circuit sizing), or extensive process variations (e.g., variability /reliability analysis and design for manufacturability). The computational challenges generated by such high-dimensional problems are generally hard to handle efficiently with traditional EDA core algorithms that are based on matrix and vector computation. This paper presents “tensor computation” as an alternative general framework for the development of efficient EDA algorithms and tools. A tensor is a high-dimensional generalization of a matrix and a vector, and is a natural choice for both storing and solving efficiently high-dimensional EDA problems. This paper gives a basic tutorial on tensors, demonstrates some recent examples of EDA applications (e.g., nonlinear circuit modeling and high-dimensional uncertainty quantification), and suggests further open EDA problems where the use of tensor computation could be of advantage.

48 citations

01 Jan 2008
TL;DR: The HOSODF can be defined for the class of causal, stable, time invariant non-linear systems which give a sinusoidal response to a specific harmonic excitation of non- linear systems.
Abstract: In this paper the concept of the Higher Order Sinusoidal Output Describing Functions (HOSODF) is presented. HOSODF can be defined for the class of causal, stable, time invariant non-linear systems which give a sinusoidal response to a specific harmonic excitation. The HOSODF relate the magnitude and phase of the individual harmonics, which together compose that specific input signal, to the sinusoidal output signal of such a system. HOSODF are the dual of the Higher Order Sinusoidal Input Describing Functions (HOSIDF). Like the HOSIDF, the HOSODF are the results of an extension of linear techniques towards non-linear systems analysis. Using the HOSODF, the non-linear systems under investigation can be modeled as a cascade of the HOSODF and a Virtual Harmonics Compressor (VHC). The VHC is defined as a non-linear component which transforms a harmonic input signal y(t) into a sinusoidal output signal y(t) with frequency ω, amplitude â and phase φ. This input signal y(t) consists of an infinite amount of harmonics of the output signal y(t) with frequency nω, amplitude â and phase nω with n = 0, 1, ...∞. Special attention is paid to the non-parametric identification of the HOSODF. The identification requires control of the frequency and amplitude of the sinusoidal output of the system within its domain of possible sinusoidal output signals. This specific state of these non-linear systems can be reached by incorporating the system under test in a feedback loop. In this loop the desired sinusoidal output is defined as the control objective of a dedicated repetitive controller consisting of a memory loop with positive feedback. The design of the learning filter required for stability is also addressed. As a spinoff of the identification technique, the authors see opportunities for advanced non-linear control of shaker systems aimed at sinusoidal excitation of non-linear systems.

7 citations

Proceedings ArticleDOI
01 Mar 1991
TL;DR: In this paper, a method for using system identification techniques to improve airframe finite element models was developed and demonstrated using linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices.
Abstract: A method for using system identification techniques to improve airframe finite element models was developed and demonstrated. The method uses linear sensitivity matrices to relate changes in selected physical parameters to changes in total system matrices. The values for these physical parameters were determined using constrained optimization with singular value decomposition. The method was confirmed using both simple and complex finite element models for which pseudo-experimental data was synthesized directly from the finite element model. The method was then applied to a real airframe model which incorporated all the complexities and details of a large finite element model and for which extensive test data was available. The method was shown to work, and the differences between the identified model and the measured results were considered satisfactory.

6 citations