The Stability Analysis of Linear Dynamical Systems with Time-Delays

Total Page:16

File Type:pdf, Size:1020Kb

The Stability Analysis of Linear Dynamical Systems with Time-Delays University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 12-2006 The Stability Analysis of Linear Dynamical Systems with Time- Delays Ajeet Ganesh Kamath University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Mechanical Engineering Commons Recommended Citation Kamath, Ajeet Ganesh, "The Stability Analysis of Linear Dynamical Systems with Time-Delays. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1985 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Ajeet Ganesh Kamath entitled "The Stability Analysis of Linear Dynamical Systems with Time-Delays." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Mechanical Engineering. VijaySekhar Chellaboina, Major Professor We have read this dissertation and recommend its acceptance: William R. Hamel, Dongjun Lee, Seddik M. Djouadi Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) To the Graduate Council: I am submitting herewith a dissertation written by Ajeet Ganesh Kamath entitled \The Stability Analysis of Linear Dynamical Systems with Time-Delays". I have examined the ¯nal electronic copy of this dissertation for form and content and rec- ommend that it be accepted in partial ful¯llment of the requirements for the degree of Doctor of Philosophy, with a major in Mechanical Engineering. VijaySekhar Chellaboina Major Professor We have read this dissertation and recommend its acceptance: William R. Hamel Dongjun Lee Seddik M. Djouadi Accepted for the Council: Linda Painter Interim Dean of Graduate Studies (Original signatures are on ¯le with o±cial student records.) The Stability Analysis of Linear Dynamical Systems with Time-Delays A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Ajeet Ganesh Kamath December 2006 Copyright °c 2006 by Ajeet Ganesh Kamath. All rights reserved. ii Dedication This dissertation is dedicated to the members of my family, who nurtured me, endured me and humored me all my life. My father, Ganesh Kamath, my mother Suman Kamath and my brother, Manju (Prashant) Kamath, thank you for all your love, support, guidance and prayers. To my ¯anc¶ee,Aparna Kher, your presence makes me strong. iii Acknowledgments First, and foremost, I am deeply indebted to my advisor, Dr. VijaySekhar Chellaboina for his guidance and advice throughout the course of my graduate education. This work is a result of his encouragement, support, ideas and constructive criticism. His willingness to support me and his guidance during my studies and research has allowed me to mature and develop as a researcher and as a person. I would also like to thank Dr. Wassim Haddad at GeorgiaTech for his support in the work that went into this research. I am also thankful to the members of my doctoral dissertation committee, Dr. Bill Hamel, Dr. Seddik Djouadi and Dr. Dongjun Lee, for their suggestions and input in this dissertation. I am also deeply thankful for all the advice and support from Dr. Gary Smith during my time at the University of Tennessee. Also, many thanks to Dr. Satish Nair and Dr. Carmen Chicone at the University of Missouri, who served on my M.S. thesis committee and with whom I took many courses. I would also like to thank all the members of my family, my father Ganesh Kamath, my mother Suman Kamath and my brother Manju (Prashant) Kamath for their love, guidance and encouragement, which have played a de¯ning role in my life. Many thanks to my ¯anc¶eeand my best friend, Aparna Kher, for being the shining light in my life, and for her support and fortitude. I look forward to spending the rest of my living years with her. In our research group, I am grateful to my colleagues and friends Jayanthy Ra- makrishnan and Alex Melin for their help and input, and for putting up with me over the years. Also deserving a special mention are the other present and past members of the research group, Hancao Li, Mithun Ranga and Sushma Kalavagunta. To all my teachers, present and past, I am deeply indebted to you for the role you have played in taking me to where I stand today. A big \thank you" to all of you. If I have omitted your names, it is only for a lack of space, and most de¯nitely not for a lack of appreciation for your e®orts. iv Abstract Time-delay systems, which are also sometimes known as hereditary systems or sys- tems with memory, aftere®ects or time-lag, represent a class of in¯nite-dimensional systems, and are used to describe, among other types of systems, propagation and transport phenomena, population dynamics, economic systems, communication net- works and neural network models. A key method for the stability analysis of time- delay dynamical systems is the Lyapunov's second method, applied to functional di®erential equations. Speci¯cally, stability of a given linear time-delay dynamical system is typically shown using a Lyapunov-Krasovskii functional, which involves a quadratic part and an integral part. The quadratic part is usually associated with the stability of the forward delay-independent part of the retarded dynamical system, but the integral part of the functional is less understood. We present a concrete method of arriving at the Lyapunov-Krasovskii functional using dissipativity theory. The stability analysis of time-delay systems has been mainly classi¯ed into two cate- gories: delay-dependent and delay-independent analysis. Delay-independent stability criteria provide su±cient conditions for stability of time-delay dynamical systems in- dependent of the amount of delay, whereas delay-dependent stability criteria provide su±cient conditions that are dependent on an upper bound of the delay. In systems where the time delay is known to be bounded, delay-dependent criteria usually give far less conservative stability predictions as compared to delay-independent results. Hence, for such systems it is of paramount importance to derive the sharpest possible delay-dependent stability margins. We show how the stability criteria may also be in- terpreted in the frequency domain in terms of a feedback interconnection of a matrix transfer function and a phase uncertainty block. We develop and present the general framework for a robust stability analysis method to account for phase uncertainties in linear systems. We present new robust stability results for time-delay systems based on pure phase information, and then, using this approach, we derive new and improved time-domain delay-dependent stability criteria for stability analysis of both retarded and neutral type time-delay systems, which we then compare with existing results in the literature. v Contents 1 Introduction 1 1.1. Motivation and Historical Overview . 1 1.2. Applications and Examples of Time-Delay Systems . 2 1.2.1. Transport and Communication Delays . 3 1.2.2. Biology and Population Dynamics . 4 1.2.3. Propagation Phenomena . 4 1.3. The Problem Statement . 5 1.4. Outline of the Dissertation . 8 1.4.1. Dissipativity Approach to Time-Delay Systems . 8 1.4.2. Dynamic Dissipativity Theory . 8 1.4.3. Structured Phase Margin . 9 1.4.4. Neutral Delay Systems . 9 1.4.5. Conclusions and Future Work . 9 2 A Dissipative Dynamical Systems Approach to the Stability Analysis of Time-Delay Systems 10 2.1. Introduction . 10 2.2. Mathematical Preliminaries . 11 2.3. Stability Theory for Continuous-Time Time-Delay Dynamical Systems using Dissipativity Theory . 14 2.4. Stability Theory for Discrete-Time Time-Delay Dynamical Systems us- ing Dissipativity Theory . 18 2.5. Conclusion . 24 3 Stability Analysis of Time Delay Systems using Dynamic Dissipa- tivity Theory 26 3.1. Introduction . 26 3.2. Mathematical Preliminaries . 27 3.3. Stability Theory for Time-Delay Dynamical Systems using Dissipativ- ity Theory . 33 3.4. Illustrative Numerical Example . 40 3.5. Conclusion . 40 vi 4 Structured Phase Margin for Stability Analysis of Time-Delay Sys- tems 42 4.1. Introduction . 42 4.2. Mathematical Preliminaries . 47 4.3. The Structured Phase Margin of a Complex Matrix . 50 4.4. A Computable Lower Bound for the Structured Phase Margin . 52 4.5. Connections between the Structured Phase Margin and the Structured Singular Value . 56 4.6. Stability of Linear Dynamical Systems with Structured Phase Uncer- tainties . 59 4.7. Stability Theory for Time Delay Dynamical Systems . 61 4.8. Time-Domain Conditions for Stability Analysis of Time-Delay Systems 67 4.9. Illustrative Numerical Examples . 70 4.10. Conclusion . 74 5 Su±cient Conditions for Stability of Neutral Time-Delay Systems using the Structured Phase Margin 75 5.1. Introduction . 75 5.2. Frequency-Domain Stability Conditions for Neutral Time-Delay Dy- namical Systems . 77 5.3. Time-Domain Test for Stability Analysis of Linear Neutral Time-Delay Systems . 81 5.4. Illustrative Numerical Examples . 82 6 Conclusions and Future Research 85 6.1. Contributions . 85 References 88 Vita 94 vii List of Tables 5.1 Maximum allowable delay prediction for Example 5.4.2 . 84 viii List of Figures 1.1 Metal strip with two Lp cells (three capacitive cells dashed) . 4 1.2 Small PEEC model for metal strip .
Recommended publications
  • 2.161 Signal Processing: Continuous and Discrete Fall 2008
    MIT OpenCourseWare http://ocw.mit.edu 2.161 Signal Processing: Continuous and Discrete Fall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms. Massachusetts Institute of Technology Department of Mechanical Engineering 2.161 Signal Processing - Continuous and Discrete Fall Term 2008 1 Lecture 1 Reading: • Class handout: The Dirac Delta and Unit-Step Functions 1 Introduction to Signal Processing In this class we will primarily deal with processing time-based functions, but the methods will also be applicable to spatial functions, for example image processing. We will deal with (a) Signal processing of continuous waveforms f(t), using continuous LTI systems (filters). a LTI dy nam ical system input ou t pu t f(t) Continuous Signal y(t) Processor and (b) Discrete-time (digital) signal processing of data sequences {fn} that might be samples of real continuous experimental data, such as recorded through an analog-digital converter (ADC), or implicitly discrete in nature. a LTI dis crete algorithm inp u t se q u e n c e ou t pu t seq u e n c e f Di screte Si gnal y { n } { n } Pr ocessor Some typical applications that we look at will include (a) Data analysis, for example estimation of spectral characteristics, delay estimation in echolocation systems, extraction of signal statistics. (b) Signal enhancement. Suppose a waveform has been contaminated by additive “noise”, for example 60Hz interference from the ac supply in the laboratory. 1copyright c D.Rowell 2008 1–1 inp u t ou t p u t ft + ( ) Fi lte r y( t) + n( t) ad d it ive no is e The task is to design a filter that will minimize the effect of the interference while not destroying information from the experiment.
    [Show full text]
  • Lecture 2 LSI Systems and Convolution in 1D
    Lecture 2 LSI systems and convolution in 1D 2.1 Learning Objectives Understand the concept of a linear system. • Understand the concept of a shift-invariant system. • Recognize that systems that are both linear and shift-invariant can be described • simply as a convolution with a certain “impulse response” function. 2.2 Linear Systems Figure 2.1: Example system H, which takes in an input signal f(x)andproducesan output g(x). AsystemH takes an input signal x and produces an output signal y,whichwecan express as H : f(x) g(x). This very general definition encompasses a vast array of di↵erent possible systems.! A subset of systems of particular relevance to signal processing are linear systems, where if f (x) g (x)andf (x) g (x), then: 1 ! 1 2 ! 2 a f + a f a g + a g 1 · 1 2 · 2 ! 1 · 1 2 · 2 for any input signals f1 and f2 and scalars a1 and a2. In other words, for a linear system, if we feed it a linear combination of inputs, we get the corresponding linear combination of outputs. Question: Why do we care about linear systems? 13 14 LECTURE 2. LSI SYSTEMS AND CONVOLUTION IN 1D Question: Can you think of examples of linear systems? Question: Can you think of examples of non-linear systems? 2.3 Shift-Invariant Systems Another subset of systems we care about are shift-invariant systems, where if f1 g1 and f (x)=f (x x )(ie:f is a shifted version of f ), then: ! 2 1 − 0 2 1 f (x) g (x)=g (x x ) 2 ! 2 1 − 0 for any input signals f1 and shift x0.
    [Show full text]
  • Introduction to Aircraft Aeroelasticity and Loads
    JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads Jan R. Wright University of Manchester and J2W Consulting Ltd, UK Jonathan E. Cooper University of Liverpool, UK iii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads i JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 ii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Introduction to Aircraft Aeroelasticity and Loads Jan R. Wright University of Manchester and J2W Consulting Ltd, UK Jonathan E. Cooper University of Liverpool, UK iii JWBK209-FM-I JWBK209-Wright November 14, 2007 2:58 Char Count= 0 Copyright C 2007 John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England Telephone (+44) 1243 779777 Email (for orders and customer service enquiries): [email protected] Visit our Home Page on www.wileyeurope.com or www.wiley.com All Rights Reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means, electronic, mechanical, photocopying, recording, scanning or otherwise, except under the terms of the Copyright, Designs and Patents Act 1988 or under the terms of a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road, London W1T 4LP, UK, without the permission in writing of the Publisher. Requests to the Publisher should be addressed to the Permissions Department, John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex PO19 8SQ, England, or emailed to [email protected], or faxed to (+44) 1243 770620.
    [Show full text]
  • Linear System Theory
    Chapter 2 Linear System Theory In this course, we will be dealing primarily with linear systems, a special class of sys- tems for which a great deal is known. During the first half of the twentieth century, linear systems were analyzed using frequency domain (e.g., Laplace and z-transform) based approaches in an effort to deal with issues such as noise and bandwidth issues in communication systems. While they provided a great deal of intuition and were suf- ficient to establish some fundamental results, frequency domain approaches presented various drawbacks when control scientists started studying more complicated systems (containing multiple inputs and outputs, nonlinearities, noise, and so forth). Starting in the 1950’s (around the time of the space race), control engineers and scientists started turning to state-space models of control systems in order to address some of these issues. These time-domain approaches are able to effectively represent concepts such as the in- ternal state of the system, and also present a method to introduce optimality conditions into the controller design procedure. We will be using the state-space (or “modern”) approach to control almost exclusively in this course, and the purpose of this chapter is to review some of the essential concepts in this area. 2.1 Discrete-Time Signals Given a field F,asignal is a mapping from a set of numbers to F; in other words, signals are simply functions of the set of numbers that they operate on. More specifically: • A discrete-time signal f is a mapping from the set of integers Z to F, and is denoted by f[k]fork ∈ Z.Eachinstantk is also known as a time-step.
    [Show full text]
  • Digital Image Processing Lectures 3 & 4
    2-D Signals and Systems 2-D Fourier Transform Digital Image Processing Lectures 3 & 4 M.R. Azimi, Professor Department of Electrical and Computer Engineering Colorado State University Spring 2017 M.R. Azimi Digital Image Processing 2-D Signals and Systems 2-D Fourier Transform Definitions and Extensions: 2-D Signals: A continuous image is represented by a function of two variables e.g. x(u; v) where (u; v) are called spatial coordinates and x is the intensity. A sampled image is represented by x(m; n). If pixel intensity is also quantized (digital images) then each pixel is represented by B bits (typically B = 8 bits/pixel). 2-D Delta Functions: They are separable 2-D functions i.e. 1 (u; v) = (0; 0) Dirac: δ(u; v) = δ(u) δ(v) = 0 Otherwise 1 (m; n) = (0; 0) Kronecker: δ(m; n) = δ(m) δ(n) = 0 Otherwise M.R. Azimi Digital Image Processing 2-D Signals and Systems 2-D Fourier Transform Properties: For 2-D Dirac delta: 1 1- R R x(u0; v0)δ(u − u0; v − v0)du0dv0 = x(u; v) −∞ 2- R R δ(u; v)du dv = 1 8 > 0 − For 2-D Kronecker delta: 1 1 1- x(m; n) = P P x(m0; n0)δ(m − m0; n − n0) m0=−∞ n0=−∞ 1 2- P P δ(m; n) = 1 m;n=−∞ Periodic Signals: Consider an image x(m; n) which satisfies x(m; n + N) = x(m; n) x(m + M; n) = x(m; n) This signal is said to be doubly periodic with horizontal and vertical periods M and N, respectively.
    [Show full text]
  • CHAPTER TWO - Static Aeroelasticity – Unswept Wing Structural Loads and Performance 21 2.1 Background
    Static aeroelasticity – structural loads and performance CHAPTER TWO - Static Aeroelasticity – Unswept wing structural loads and performance 21 2.1 Background ........................................................................................................................... 21 2.1.2 Scope and purpose ....................................................................................................................... 21 2.1.2 The structures enterprise and its relation to aeroelasticity ............................................................ 22 2.1.3 The evolution of aircraft wing structures-form follows function ................................................ 24 2.2 Analytical modeling............................................................................................................... 30 2.2.1 The typical section, the flying door and Rayleigh-Ritz idealizations ................................................ 31 2.2.2 – Functional diagrams and operators – modeling the aeroelastic feedback process ....................... 33 2.3 Matrix structural analysis – stiffness matrices and strain energy .......................................... 34 2.4 An example - Construction of a structural stiffness matrix – the shear center concept ........ 38 2.5 Subsonic aerodynamics - fundamentals ................................................................................ 40 2.5.1 Reference points – the center of pressure..................................................................................... 44 2.5.2 A different
    [Show full text]
  • Solution Bounds, Stability and Attractors' Estimates of Some Nonlinear Time-Varying Systems
    1 Solution Bounds, Stability and Attractors’ Estimates of Some Nonlinear Time-Varying Systems Mark A. Pinsky and Steve Koblik Abstract. Estimation of solution norms and stability for time-dependent nonlinear systems is ubiquitous in numerous applied and control problems. Yet, practically valuable results are rare in this area. This paper develops a novel approach, which bounds the solution norms, derives the corresponding stability criteria, and estimates the trapping/stability regions for a broad class of the corresponding systems. Our inferences rest on deriving a scalar differential inequality for the norms of solutions to the initial systems. Utility of the Lipschitz inequality linearizes the associated auxiliary differential equation and yields both the upper bounds for the norms of solutions and the relevant stability criteria. To refine these inferences, we introduce a nonlinear extension of the Lipschitz inequality, which improves the developed bounds and estimates the stability basins and trapping regions for the corresponding systems. Finally, we conform the theoretical results in representative simulations. Key Words. Comparison principle, Lipschitz condition, stability criteria, trapping/stability regions, solution bounds AMS subject classification. 34A34, 34C11, 34C29, 34C41, 34D08, 34D10, 34D20, 34D45 1. INTRODUCTION. We are going to study a system defined by the equation n (1) xAtxftxFttt , , [0 , ), x , ft ,0 0 where matrix A nn and functions ft:[ , ) nn and F : n are continuous and bounded, and At is also continuously differentiable. It is assumed that the solution, x t, x0 , to the initial value problem, x t0, x 0 x 0 , is uniquely defined for tt0 . Note that the pertained conditions can be found, e.g., in [1] and [2].
    [Show full text]
  • Strange Attractors and Classical Stability Theory
    Nonlinear Dynamics and Systems Theory, 8 (1) (2008) 49–96 Strange Attractors and Classical Stability Theory G. Leonov ∗ St. Petersburg State University Universitetskaya av., 28, Petrodvorets, 198504 St.Petersburg, Russia Received: November 16, 2006; Revised: December 15, 2007 Abstract: Definitions of global attractor, B-attractor and weak attractor are introduced. Relationships between Lyapunov stability, Poincare stability and Zhukovsky stability are considered. Perron effects are discussed. Stability and instability criteria by the first approximation are obtained. Lyapunov direct method in dimension theory is introduced. For the Lorenz system necessary and sufficient conditions of existence of homoclinic trajectories are obtained. Keywords: Attractor, instability, Lyapunov exponent, stability, Poincar´esection, Hausdorff dimension, Lorenz system, homoclinic bifurcation. Mathematics Subject Classification (2000): 34C28, 34D45, 34D20. 1 Introduction In almost any solid survey or book on chaotic dynamics, one encounters notions from classical stability theory such as Lyapunov exponent and characteristic exponent. But the effect of sign inversion in the characteristic exponent during linearization is seldom mentioned. This effect was discovered by Oscar Perron [1], an outstanding German math- ematician. The present survey sets forth Perron’s results and their further development, see [2]–[4]. It is shown that Perron effects may occur on the boundaries of a flow of solutions that is stable by the first approximation. Inside the flow, stability is completely determined by the negativeness of the characteristic exponents of linearized systems. It is often said that the defining property of strange attractors is the sensitivity of their trajectories with respect to the initial data. But how is this property connected with the classical notions of instability? For continuous systems, it was necessary to remember the almost forgotten notion of Zhukovsky instability.
    [Show full text]
  • Chapter 8 Stability Theory
    Chapter 8 Stability theory We discuss properties of solutions of a first order two dimensional system, and stability theory for a special class of linear systems. We denote the independent variable by ‘t’ in place of ‘x’, and x,y denote dependent variables. Let I ⊆ R be an interval, and Ω ⊆ R2 be a domain. Let us consider the system dx = F (t, x, y), dt (8.1) dy = G(t, x, y), dt where the functions are defined on I × Ω, and are locally Lipschitz w.r.t. variable (x, y) ∈ Ω. Definition 8.1 (Autonomous system) A system of ODE having the form (8.1) is called an autonomous system if the functions F (t, x, y) and G(t, x, y) are constant w.r.t. variable t. That is, dx = F (x, y), dt (8.2) dy = G(x, y), dt Definition 8.2 A point (x0, y0) ∈ Ω is said to be a critical point of the autonomous system (8.2) if F (x0, y0) = G(x0, y0) = 0. (8.3) A critical point is also called an equilibrium point, a rest point. Definition 8.3 Let (x(t), y(t)) be a solution of a two-dimensional (planar) autonomous system (8.2). The trace of (x(t), y(t)) as t varies is a curve in the plane. This curve is called trajectory. Remark 8.4 (On solutions of autonomous systems) (i) Two different solutions may represent the same trajectory. For, (1) If (x1(t), y1(t)) defined on an interval J is a solution of the autonomous system (8.2), then the pair of functions (x2(t), y2(t)) defined by (x2(t), y2(t)) := (x1(t − s), y1(t − s)), for t ∈ s + J (8.4) is a solution on interval s + J, for every arbitrary but fixed s ∈ R.
    [Show full text]
  • Robot Dynamics and Control Lecture 8: Basic Lyapunov Stability Theory
    MCE/EEC 647/747: Robot Dynamics and Control Lecture 8: Basic Lyapunov Stability Theory Reading: SHV Appendix Mechanical Engineering Hanz Richter, PhD MCE503 – p.1/17 Stability in the sense of Lyapunov A dynamic system x˙ = f(x) is Lyapunov stable or internally stable about an equilibrium point xeq if state trajectories are confined to a bounded region whenever the initial condition x0 is chosen sufficiently close to xeq. Mathematically, given R> 0 there always exists r > 0 so that if ||x0 − xeq|| <r, then ||x(t) − xeq|| <R for all t> 0. As seen in the figure R defines a desired confinement region, while r defines the neighborhood of xeq where x0 must belong so that x(t) does not exit the confinement region. R r xeq x0 x(t) MCE503 – p.2/17 Stability in the sense of Lyapunov... Note: Lyapunov stability does not require ||x(t)|| to converge to ||xeq||. The stronger definition of asymptotic stability requires that ||x(t)|| → ||xeq|| as t →∞. Input-Output stability (BIBO) does not imply Lyapunov stability. The system can be BIBO stable but have unbounded states that do not cause the output to be unbounded (for example take x1(t) →∞, with y = Cx = [01]x). The definition is difficult to use to test the stability of a given system. Instead, we use Lyapunov’s stability theorem, also called Lyapunov’s direct method. This theorem is only a sufficient condition, however. When the test fails, the results are inconclusive. It’s still the best tool available to evaluate and ensure the stability of nonlinear systems.
    [Show full text]
  • Calculus and Differential Equations II
    Calculus and Differential Equations II MATH 250 B Linear systems of differential equations Linear systems of differential equations Calculus and Differential Equations II Second order autonomous linear systems We are mostly interested with2 × 2 first order autonomous systems of the form x0 = a x + b y y 0 = c x + d y where x and y are functions of t and a, b, c, and d are real constants. Such a system may be re-written in matrix form as d x x a b = M ; M = : dt y y c d The purpose of this section is to classify the dynamics of the solutions of the above system, in terms of the properties of the matrix M. Linear systems of differential equations Calculus and Differential Equations II Existence and uniqueness (general statement) Consider a linear system of the form dY = M(t)Y + F (t); dt where Y and F (t) are n × 1 column vectors, and M(t) is an n × n matrix whose entries may depend on t. Existence and uniqueness theorem: If the entries of the matrix M(t) and of the vector F (t) are continuous on some open interval I containing t0, then the initial value problem dY = M(t)Y + F (t); Y (t ) = Y dt 0 0 has a unique solution on I . In particular, this means that trajectories in the phase space do not cross. Linear systems of differential equations Calculus and Differential Equations II General solution The general solution to Y 0 = M(t)Y + F (t) reads Y (t) = C1 Y1(t) + C2 Y2(t) + ··· + Cn Yn(t) + Yp(t); = U(t) C + Yp(t); where 0 Yp(t) is a particular solution to Y = M(t)Y + F (t).
    [Show full text]
  • The Scientist and Engineer's Guide to Digital Signal Processing Properties of Convolution
    CHAPTER 7 Properties of Convolution A linear system's characteristics are completely specified by the system's impulse response, as governed by the mathematics of convolution. This is the basis of many signal processing techniques. For example: Digital filters are created by designing an appropriate impulse response. Enemy aircraft are detected with radar by analyzing a measured impulse response. Echo suppression in long distance telephone calls is accomplished by creating an impulse response that counteracts the impulse response of the reverberation. The list goes on and on. This chapter expands on the properties and usage of convolution in several areas. First, several common impulse responses are discussed. Second, methods are presented for dealing with cascade and parallel combinations of linear systems. Third, the technique of correlation is introduced. Fourth, a nasty problem with convolution is examined, the computation time can be unacceptably long using conventional algorithms and computers. Common Impulse Responses Delta Function The simplest impulse response is nothing more that a delta function, as shown in Fig. 7-1a. That is, an impulse on the input produces an identical impulse on the output. This means that all signals are passed through the system without change. Convolving any signal with a delta function results in exactly the same signal. Mathematically, this is written: EQUATION 7-1 The delta function is the identity for ( ' convolution. Any signal convolved with x[n] *[n] x[n] a delta function is left unchanged. This property makes the delta function the identity for convolution. This is analogous to zero being the identity for addition (a%0 ' a), and one being the identity for multiplication (a×1 ' a).
    [Show full text]