View Example 45

Total Page:16

File Type:pdf, Size:1020Kb

View Example 45 UNIVERSITY OF CINCINNATI _____________ , 20 _____ I,______________________________________________, hereby submit this as part of the requirements for the degree of: ________________________________________________ in: ________________________________________________ It is entitled: ________________________________________________ ________________________________________________ ________________________________________________ ________________________________________________ Approved by: ________________________ ________________________ ________________________ ________________________ ________________________ Stability Analysis and Design of Servo-Hydraulic Systems - A Bifurcation Study A dissertation submitted to the Division of Research and Advanced Studies of the University of Cincinnati in partial fulfillment of the requirements for the degree of DOCTORATE OF PHILOSOPHY (Ph.D.) in the Department of Mechanical, Industrial and Nuclear Engineering of the College of Engineering 2002 by Amit Shukla B.E.M.E., MNR Engineering College, India 1996 M.S.M.E., University of Cincinnati, 1998 Committee Chair: Dr. David F. Thompson Abstract Design for robust stability is one of the most important issues in nonlinear systems theory. The validity of linear system design in a small neighborhood is not a sufficient criterion for systems that undergo parametric variations and have strong nonlinear characteristics. With rapid growth in the systems theory, the design of nonlinear systems using bifurcation theory- based procedures has been one of the key developments. Servo-hydraulic systems are one of the most commonly used actuation and control devices, due to their force to weight ratio. They also are highly nonlinear in nature and hence provide considerable difficulty in the design and analysis of these systems and their control algorithms. The goal of this dissertation is to tackle some of the issues of the nonlinear systems theory with applications to servo-hydraulic systems. The use of bifurcation theory for the design and analysis of a nonlinear system is illustrated, and a detailed investigation into the dynamics associated with the servo-hydraulic systems is done. Further, the model decomposition/reduction strategy for parametric study in the nonlinear system is suggested. The idea of control-induced bifurcation is introduced and explained in light of servo-hydraulic systems. The servo-hydraulic system nonlinearities are explained and their effects on the robust stability are highlighted. This numerical work is also complemented with the experimental results on the servo-hydraulic circuits. This general procedure for robust stability design and control design, under the influence of nonlinearities, presented in this work can be used for any nonlinear system. The limitations of bifurcation theory based tools are also highlighted. Acknowledgements This work has been possible due to motivation and support of various individuals who gave me sound advice and guidance at numerous occasions throughout my years as a graduate student at the University of Cincinnati. I express my deepest gratitude to my academic advisor Dr. David F. Thompson, whose excellent guidance and critical comments paved the way for this work as well as contributed to my development as an academician. He has been a true guide, a mentor and a friend. I also wish to acknowledge Dr. Randall J. Allemang, Dr. Ronald Huston, and Dr. Edward J. Berger for being on my dissertation committee and for providing valuable help and guidance. All of them have shaped my graduate education and life very significantly by their great teaching and thoughtful concerns. I am also grateful to the members of UC SDRL, specially, Dr. David Brown, Dr. Jay Kim, Dr. Allyn Phillips, Dr. Gregory Kremer, Dr. Doug Adams, Dr. Bill Fladung, Dan Lazor, Srinivas Kowta, Tom Terrell, Jeff Hylok and Bruce Fouts who made my stay at UC most enjoyable. I also would like to thank Rhonda Christman for her exceptional secretarial help during my 6 years at UC. I am also thankful to the National Science Foundation for providing monetary support for this work and Ford Motor Company (Dr. Gregory M. Pietron) for support and fruitful discussions on hydraulic system modeling. This has been a project which would be impossible without unflinching support of my family. I dedicate this work to my parents. They provided an atmosphere and everything else in my upbringing for me to reach this point in my life. The support and understanding that my wife gave me provided extra boost for completion of this work. Her love has been my strength for the past few years. Her parents were also very cooperative and patient regarding my tenure as a graduate student. Table of Contents Abstract List of Figures 4 List of Symbols 9 1. Introduction 11 1.1. Background/Motivation 11 1.2. Applications 16 1.3. Organization of the dissertation 17 2. Servo hydraulic drives 20 2.1. Previous work 20 2.2. Fundamentals of hydraulic system modeling 23 2.2.1. Orifice flows 23 2.2.2. Continuity equations 24 2.2.3. Example 25 2.2.4. Valve modeling 26 2.3. Modeling assumptions 33 2.4. Physically realistic models of servo hydraulic system 34 2.5. Modeling of servo-pump actuator system 37 2.6. Modeling of servo-valve actuator system 39 2.7. Servo-valve actuator model with line dynamics 42 3. Linear and nonlinear analysis of servo-hydraulic systems 45 3.1. Linear analysis of servo-hydraulic system –a review example 45 3.2. Experimental results for servo-hydraulic systems – a review 47 3.3. Nonlinearities in the servo-hydraulic systems and their effect on stability 48 1 3.3.1. Effect of flow nonlinearity on the stability 48 3.3.2. Effect of saturation on the stability 50 3.4. Large scale coupled nonlinear systems 50 3.5. Why nonlinear analysis is needed? 52 4. Nonlinear systems analysis and bifurcation theory 53 4.1. Dynamical systems and equilibrium points 54 4.2. Generic bifurcations 57 4.3. Global bifurcations, jumps and non-local behavior 62 4.4. Stability of forced systems-feedback loop nonlinearities and nonlinear feedback 63 4.5. Nonlinear systems analysis tools 64 5. Nonlinear systems analysis –a bifurcation theory based approach 66 5.1. Previous work 66 5.2. Multi-parameter multi-space bifurcation theory 71 5.2.1. Parameter space, eigen-space and state space 74 5.2.2. Constant velocity solutions 76 5.2.3. Robust bifurcation stability analysis 77 5.3. Decomposition of the system 80 5.3.1. Fastest unstable mode with associated parameters 81 5.3.2. Transformations for parametric model decomposition 82 5.4. Control induced bifurcations 83 5.5. Control of bifurcation instabilities 85 5.6. Measure of nonlinearity 86 5.7. Numerical aspects of bifurcation analysis 87 5.7.1. Data noise and nonlinear effects 89 6. Preliminary bifurcation results: numerical 90 2 6.1. Characterization of nonlinear dynamics- computational results 90 6.2. Results for servo-pump actuator system 90 6.2.1. Simulation studies 91 6.2.2. Parametric bifurcation studies 94 6.3. Results for the servo-valve actuator model 101 6.4. Chapter summary 104 7. Development of experimental apparatus 125 7.1. Development of the test stand 125 7.2. Servo-valve model development and refinement 127 7.2.1. Pressure-flow-voltage nonlinear static characteristics 129 7.2.2. Linear transfer function model of the servo-valve 131 7.2.3. Servo-valve actuator model and real-time control schematic 133 7.3. Effect of accumulators 137 7.4. Chapter summary 139 8. Control Studies- Introduction 140 8.1. PD controller 140 8.2. Effect of PD controller on the servo-valve actuator system 141 8.3. Effect of 5th order linear controller on the servo-valve actuator system 161 8.4. Chapter summary 177 9. Conclusions and suggestions for future research 180 10. Bibliography 184 11. Appendix 198 3 List of Figures 2.1 A sample hydraulic system model 25 2.2 A valve schematic 27 2.3 Port flow area schematic of a spool valve 28 2.4 Open valve port area configuration 29 2.5 Closed valve configuration 30 2.6 Port flow area as a function of the valve displacement 30 2.7 A common four way spool valve 31 2.8 Axial and lateral components of flow forces in a valve port 32 2.9 Model 1, servo-pump actuator system 38 2.10 Block diagram of model 1 38 2.11 Servo-valve actuator model schematic with external loads 41 2.12 Pipe line model schematic 43 3.1 Proportional feedback for the linearized servo-pump actuator model 46 3.2 Root locus for varying the proportional feedback gain 46 3.3 Dead zone and saturation nonlinearity-input and response 50 3.4 Parameter space investigation using simulation 51 4.1 Time response, phase plane and eigenplane 55 4.2 Invariant subspaces and manifolds 56 4.3 Saddle node bifurcation plot representing the behavior of equilibrium point 58 4.4 Trans critical bifurcation 59 4.5 Pitch fork bifurcation 59 4.6 Super critical Hopf bifurcation 60 4.7 Soft and hard generation of limit cycles 60 4.8 Transition from stationary to periodic solution, via Hopf Bifurcations 62 4 4.9 Nonlinear system decomposition- nonlinear feedback 63 5.1 Parameter space, state space and eigenspace 75 5.2 Block diagram of the state space system with proportional output feedback law 78 5.3 Decomposition of system for robust stability analysis 80 5.4 Block diagram of plant and controller depicting extended parameter vector 84 5.5 Control induced bifurcation and distance to bifurcation 84 5.6 Parametric step size: large steps may skip some details 87 6.1 Eigenvalues of the nominal open loop servo-pump actuator model 92 6.2 Open loop simulation response
Recommended publications
  • Approximating Stable and Unstable Manifolds in Experiments
    PHYSICAL REVIEW E 67, 037201 ͑2003͒ Approximating stable and unstable manifolds in experiments Ioana Triandaf,1 Erik M. Bollt,2 and Ira B. Schwartz1 1Code 6792, Plasma Physics Division, Naval Research Laboratory, Washington, DC 20375 2Department of Mathematics and Computer Science, Clarkson University, P.O. Box 5815, Potsdam, New York 13699 ͑Received 5 August 2002; revised manuscript received 23 December 2002; published 12 March 2003͒ We introduce a procedure to reveal invariant stable and unstable manifolds, given only experimental data. We assume a model is not available and show how coordinate delay embedding coupled with invariant phase space regions can be used to construct stable and unstable manifolds of an embedded saddle. We show that the method is able to capture the fine structure of the manifold, is independent of dimension, and is efficient relative to previous techniques. DOI: 10.1103/PhysRevE.67.037201 PACS number͑s͒: 05.45.Ac, 42.60.Mi Many nonlinear phenomena can be explained by under- We consider a basin saddle of Eq. ͑1͒ which lies on the standing the behavior of the unstable dynamical objects basin boundary between a chaotic attractor and a period-four present in the dynamics. Dynamical mechanisms underlying periodic attractor. The chosen parameters are in a region in chaos may be described by examining the stable and unstable which the chaotic attractor disappears and only a periodic invariant manifolds corresponding to unstable objects, such attractor persists along with chaotic transients due to inter- as saddles ͓1͔. Applications of the manifold topology have secting stable and unstable manifolds of the basin saddle.
    [Show full text]
  • Hartman-Grobman and Stable Manifold Theorem
    THE HARTMAN-GROBMAN AND STABLE MANIFOLD THEOREM SLOBODAN N. SIMIC´ This is a summary of some basic results in dynamical systems we discussed in class. Recall that there are two kinds of dynamical systems: discrete and continuous. A discrete dynamical system is map f : M → M of some space M.A continuous dynamical system (or flow) is a collection of maps φt : M → M, with t ∈ R, satisfying φ0(x) = x and φs+t(x) = φs(φt(x)), for all x ∈ M and s, t ∈ R. We call M the phase space. It is usually either a subset of a Euclidean space, a metric space or a smooth manifold (think of surfaces in 3-space). Similarly, f or φt are usually nice maps, e.g., continuous or differentiable. We will focus on smooth (i.e., differentiable any number of times) discrete dynamical systems. Let f : M → M be one such system. Definition 1. The positive or forward orbit of p ∈ M is the set 2 O+(p) = {p, f(p), f (p),...}. If f is invertible, we define the (full) orbit of p by k O(p) = {f (p): k ∈ Z}. We can interpret a point p ∈ M as the state of some physical system at time t = 0 and f k(p) as its state at time t = k. The phase space M is the set of all possible states of the system. The goal of the theory of dynamical systems is to understand the long-term behavior of “most” orbits. That is, what happens to f k(p), as k → ∞, for most initial conditions p ∈ M? The first step towards this goal is to understand orbits which have the simplest possible behavior, namely fixed and periodic ones.
    [Show full text]
  • Chapter 3 Hyperbolicity
    Chapter 3 Hyperbolicity 3.1 The stable manifold theorem In this section we shall mix two typical ideas of hyperbolic dynamical systems: the use of a linear approxi- mation to understand the behavior of nonlinear systems, and the examination of the behavior of the system along a reference orbit. The latter idea is embodied in the notion of stable set. Denition 3.1.1: Let (X, f) be a dynamical system on a metric space X. The stable set of a point x ∈ X is s ∈ k k Wf (x)= y X lim d f (y),f (x) =0 . k→∞ ∈ s In other words, y Wf (x) if and only if the orbit of y is asymptotic to the orbit of x. If no confusion will arise, we drop the index f and write just W s(x) for the stable set of x. Clearly, two stable sets either coincide or are disjoint; therefore X is the disjoint union of its stable sets. Furthermore, f W s(x) W s f(x) for any x ∈ X. The twin notion of unstable set for homeomorphisms is dened in a similar way: Denition 3.1.2: Let f: X → X be a homeomorphism of a metric space X. Then the unstable set of a ∈ point x X is u ∈ k k Wf (x)= y X lim d f (y),f (x) =0 . k→∞ u s In other words, Wf (x)=Wf1(x). Again, unstable sets of homeomorphisms give a partition of the space. On the other hand, stable sets and unstable sets (even of the same point) might intersect, giving rise to interesting dynamical phenomena.
    [Show full text]
  • Invariant Manifolds and Their Interactions
    Understanding the Geometry of Dynamics: Invariant Manifolds and their Interactions Hinke M. Osinga Department of Mathematics, University of Auckland, Auckland, New Zealand Abstract Dynamical systems theory studies the behaviour of time-dependent processes. For example, simulation of a weather model, starting from weather conditions known today, gives information about the weather tomorrow or a few days in advance. Dynamical sys- tems theory helps to explain the uncertainties in those predictions, or why it is pretty much impossible to forecast the weather more than a week ahead. In fact, dynamical sys- tems theory is a geometric subject: it seeks to identify critical boundaries and appropriate parameter ranges for which certain behaviour can be observed. The beauty of the theory lies in the fact that one can determine and prove many characteristics of such bound- aries called invariant manifolds. These are objects that typically do not have explicit mathematical expressions and must be found with advanced numerical techniques. This paper reviews recent developments and illustrates how numerical methods for dynamical systems can stimulate novel theoretical advances in the field. 1 Introduction Mathematical models in the form of systems of ordinary differential equations arise in numer- ous areas of science and engineering|including laser physics, chemical reaction dynamics, cell modelling and control engineering, to name but a few; see, for example, [12, 14, 35] as en- try points into the extensive literature. Such models are used to help understand how different types of behaviour arise under a variety of external or intrinsic influences, and also when system parameters are changed; examples include finding the bursting threshold for neurons, determin- ing the structural stability limit of a building in an earthquake, or characterising the onset of synchronisation for a pair of coupled lasers.
    [Show full text]
  • Computation of Stable and Unstable Manifolds of Hyperbolic Trajectories in Two-Dimensional, Aperiodically Time-Dependent Vector fields Ana M
    Physica D 182 (2003) 188–222 Computation of stable and unstable manifolds of hyperbolic trajectories in two-dimensional, aperiodically time-dependent vector fields Ana M. Mancho a, Des Small a, Stephen Wiggins a,∗, Kayo Ide b a School of Mathematics, University of Bristol, University Walk, Bristol, BS8 1TW, UK b Department of Atmospheric Sciences, and Institute of Geophysics and Planetary Physics, UCLA, Los Angeles, CA 90095-1565, USA Received 14 October 2002; received in revised form 29 January 2003; accepted 31 March 2003 Communicated by E. Kostelich Abstract In this paper, we develop two accurate and fast algorithms for the computation of the stable and unstable manifolds of hyperbolic trajectories of two-dimensional, aperiodically time-dependent vector fields. First we develop a benchmark method in which all the trajectories composing the manifold are integrated from the neighborhood of the hyperbolic trajectory. This choice, although very accurate, is not fast and has limited usage. A faster and more powerful algorithm requires the insertion of new points in the manifold as it evolves in time. Its numerical implementation requires a criterion for determining when to insert those points in the manifold, and an interpolation method for determining where to insert them. We compare four different point insertion criteria and four different interpolation methods. We discuss the computational requirements of all of these methods. We find two of the four point insertion criteria to be accurate and robust. One is a variant of a criterion originally proposed by Hobson. The other is a slight variant of a method due to Dritschel and Ambaum arising from their studies of contour dynamics.
    [Show full text]
  • INVARIANT MANIFOLDS AS PULLBACK ATTRACTORS of NONAUTONOMOUS DIFFERENTIAL EQUATIONS Bernd Aulbach1 and Martin Rasmussen2 Stefan S
    DISCRETE AND CONTINUOUS Website: http://aimSciences.org DYNAMICAL SYSTEMS Volume 15, Number 2, June 2006 pp. 579-596 INVARIANT MANIFOLDS AS PULLBACK ATTRACTORS OF NONAUTONOMOUS DIFFERENTIAL EQUATIONS Bernd Aulbach1 and Martin Rasmussen2 Department of Mathematics University of Augsburg D-86135 Augsburg, Germany Stefan Siegmund3 Department of Mathematics University of Frankfurt D-60325 Frankfurt am Main, Germany (Communicated by Glenn F. Webb) Abstract. We discuss the relationship between invariant manifolds of nonau- tonomous di®erential equations and pullback attractors. This relationship is essential, e.g., for the numerical approximation of these manifolds. In the ¯rst step, we show that the unstable manifold is the pullback attractor of the di®er- ential equation. The main result says that every (hyperbolic or nonhyperbolic) invariant manifold is the pullback attractor of a related system which we con- struct explicitly using spectral transformations. To illustrate our theorem, we present an application to the Lorenz system and approximate numerically the stable as well as the strong stable manifold of the origin. 1. Introduction. In the theory of dynamical systems (e.g., autonomous di®eren- tial equations) the stable and unstable manifold theorem|¯rst proved by Poincar¶e [13] and Hadamard [10]|plays an important role in the study of the flow near a hyperbolic rest point or a periodic solution. The idea is to show that the rest point x = 0, say, locally inherits the dynamical structure of its hyperbolic linearization xAx_ , i.e., the spectrum s u σ(A) = σ (A) [ σ (A)f¸1; : : : ; ¸rg [ f¸r+1; : : : ; ¸ng consists of eigenvalues ¸1; : : : ; ¸r with negative real parts and ¸r+1; : : : ; ¸n with positive real parts.
    [Show full text]
  • Matrix Algebra I N THIS APPENDIX We Review Important Concepts From
    ApPENDIX A I Matrix Algebra IN THIS APPENDIX we review important concepts from matrix algebra. A.I EIGENVALUES AND EIGENVECTORS Definition A.I LetA be an m X m matrix and x a nonzero m-dimensional real or complex vector. If Ax = Ax for some real or complex number A, then A is called an eigenvalue of A and x the corresponding eigenvector. For example, the matrix A = (~ ~) has an eigenvector (~), and cor­ responding eigenvalue 4. 557 MATRIX ALGEBRA Eigenvalues are found as roots A of the characteristic polynomial det(A - AI). If A is an eigenvalue of A, then any nonzero vector in the nullspace of A - AI is an eigenvector corresponding to A. For this example, det(A - AI) = det e; A 2 ~ A) = (A - l)(A - 2) - 6 = (A - 4)(A + 1), (A1) so the eigenvalues are A = 4, -1. The eigenvectors corresponding to A = 4 are found in the nullspace of = (A2) A- 41 (-32 -23) and so consist of all nonzero multiples of ( ~ ) . Similarly, the eigenvectors corre­ sponding to A = -1 are all nonzero multiples of ( _ ~ ) . If A and Bare m X m matrices, then the set of eigenvalues of the product matrix AB is the same as the set of eigenvalues of the product BA In fact, let A be an eigenvalue of AB, so that ABx = Ax for some x '* O. If A = 0, then 0= det(AB - AI) = det(AB) = (detA)(detB) = det(BA - AI), so A = 0 is also an eigenvalue of BA If A '* 0, then ABx = Ax implies BA(Bx) = ABx.
    [Show full text]
  • An Introduction to Dynamical Systems
    CHAOS An Introduction to Dynamical Systems KATHLEEN T. ALLIGOOD George Mason University TIM D. SAUER George Mason University JAMES A. YORKE University of Maryland Springer - f Contents INTRODUCTION ONE-DIMENSIONAL MAPS I 1.1 One-Dimensional Maps 2 1.2 Cobweb Plot: Graphical Representation of an Orbit 5 1.3 Stability of Fixed Points 9 1.4 Periodic Points 13 1.5 The Family of Logistic Maps 17 1.6 The Logistic Map G(x) = 4x(l - x) 22 1.7 Sensitive Dependence on Initial Conditions 25 1.8 Itineraries 27 CHALLENGE 1: PERIOD THREE IMPLIES CHAOS 32 EXERCISES 36 LAB VISIT 1: BOOM, BUST, AND CHAOS IN THE BEETLE CENSUS 39 XIII CONTENTS TWO-DIMENSIONAL MAPS 43 2.1 Mathematical Models 44 2.2 Sinks, Sources, and Saddles 58 2.3 Linear Maps 62 2.4 Coordinate Changes 67 2.5 Nonlinear Maps and the Jacobian Matrix 68 2.6 Stable and Unstable Manifolds 78 2.7 Matrix Times Circle Equals Ellipse 87 CHALLENGE 2: COUNTING THE PERIODIC ORBITS OF LINEAR MAPS ON A TORUS 92 EXERCISES 98 LAB VISIT 2: Is THE SOLAR SYSTEM STABLE? 99 CHAOS 105 3.1 Lyapunov Exponents 106 3.2 Chaotic Orbits 109 3.3 Conjugacy and the Logistic Map 114 3.4 Transition Graphs and Fixed Points 124 3.5 Basins of Attraction 129 CHALLENGE 3: SHARKOVSKII'S THEOREM 135 EXERCISES 140 LAB VISIT 3: PERIODICITY AND CHAOS IN A CHEMICAL REACTION 143 FRACTALS 149 4.1 Cantor Sets 150 4-2 Probabilistic Constructions of Fractals 156 4-3 Fractals from Deterministic Systems 161 4-4 Fractal Basin Boundaries 164 4-5 Fractal Dimension 172 4-6 Computing the Box-Counting Dimension 177 4-7 Correlation Dimension 180
    [Show full text]
  • Bifurcation Theory
    Bifurcation Theory Ale Jan Homburg The axiomatization and algebraization of mathematics has led to the illegibility of such a large number of mathematical texts that the threat of complete loss of contact with physics and the natural sciences has been realized. (Vladimir Arnold) Contents 1 Introduction 5 1.1 The Rosenzweig-MacArthur model...............................8 1.2 Exercises............................................. 12 2 Elements of nonlinear analysis 17 2.1 Calculus.............................................. 17 2.1.1 Nemytskii operators................................... 19 2.2 Implicit function theorem.................................... 21 2.2.1 Applications of the implicit function theorem..................... 23 2.3 Lyapunov-Schmidt reduction.................................. 24 2.4 Floquet theory.......................................... 26 2.5 Poincar´ereturn maps...................................... 30 2.6 Center manifolds......................................... 33 2.7 Normal forms........................................... 36 2.8 Manifolds and transversality................................... 41 2.9 Sard's theorem.......................................... 42 2.10 Exercises............................................. 46 3 Local bifurcations 51 3.1 The saddle-node bifurcation................................... 51 3.1.1 One dimensional saddle-node bifurcation........................ 51 3.1.2 Higher dimensional saddle-node bifurcation...................... 53 3.1.3 Saddle-node bifurcation of periodic orbits......................
    [Show full text]
  • Local Stable Manifold of Of
    A NEW PROOF OF THE STABLE MANIFOLD THEOREM FOR HYPERBOLIC FIXED POINTS ON SURFACES MARK HOLLAND AND STEFANO LUZZATTO ABSTRACT. We introduce a new technique for proving the classical Stable Manifold theorem for hyperbolic fixed points. This method is much more geometrical than the standard approaches which rely on abstract fixed point theorems. It is based on the convergence of a canonical sequence of “finite time local stable manifolds” which are related to the dynamics of a finite number of iterations. 1. INTRODUCTION 1.1. Stable sets. Let be a two-dimensional Riemannian manifold with Riemannian metric ¡ , ¥ ¦¨§ © and let ¢¤£ be a diffeomorphism. Suppose that is a fixed point. A classical question concerns the effect that the existence of such a fixed point has on the global dynamics of . In particular we shall concentrate here on some properties of the set of points which are forward asymptotic to © . © Definition 1. The global stable set © of is ¥ # ¥ %'&( © £¡¢ !"© $ as In general )© can be extremely complicated, both in its intrinsic geometry [14] and/or in the way it is embedded in [18, 19, 22] It is useful therefore to begin with a study of that part # *© © +-, of which remains in a fixed neighbourhood of for all forward iterations. For and 1)2 $-.0/ let # & 5 43 £¡6¢879:!"6©<;=+?> ;A@B;C$ED=/ " 12GF and 1)2 ( 5 5 3 IH 43 JLK # © Definition 2. For +M, , define the local stable set of by 2GF 1 ( 5 5 3 © ©N *©PO In several situations it is possible to obtain a fairly comprehensive description of the geomet- rical and dynamical properties of the local stable manifold.
    [Show full text]
  • REPRESENTATIONS of INVARIANT MANIFOLDS for APPLICATIONS in SYSTEM-TO-SYSTEM TRANSFER DESIGN a Thesis Submitted to the Faculty Of
    REPRESENTATIONS OF INVARIANT MANIFOLDS FOR APPLICATIONS IN SYSTEM-TO-SYSTEM TRANSFER DESIGN A Thesis Submitted to the Faculty of Purdue University by Christopher E. Patterson In Partial Fulfillment of the Requirements for the Degree of Master of Science May 2005 ii For Betty and Ruth. iii ACKNOWLEDGMENTS First, I thank my parents. They have been with me through some very difficult times and supported me in more ways than I can mention. I could not have succeeded in school or in life without their encouragement. Because research does not occur in a vacuum, I thank all the past and current members of my research group for their interest in my work and for their help. Their friendship is greatly appreciated and I hope I succeed in returning the favor. I thank my adviser, Professor K. C. Howell, for her guidence throughout my graduate career. Her motivation has been invaluable. Finally, I thank the School of Aeronautics and Astronautics at Purdue University for support of my research and my education. Portions of this work were supported by NASA and the Goddard Mission Services Evolution Center under contract numbers NCC5-358 and NAG5-11839. iv TABLE OF CONTENTS Page LIST OF FIGURES ................................ vii ABSTRACT .................................... x 1 Introduction ................................... 1 1.1 Problem Definition ............................ 2 1.2 Previous Contributions .......................... 5 1.2.1 Three-Body Problem ....................... 5 1.2.2 Approximations and Transfers .................. 8 1.3 Current Work ............................... 10 2 BACKGROUND: MATHEMATICAL MODELS ............... 12 2.1 The Circular Restricted Three-Body Problem ............. 13 2.1.1 Assumptions ............................ 13 2.1.2 Reference Frames ......................... 14 2.1.3 Characteristic Quantities ....................
    [Show full text]
  • Stability I: Equilibrium Points
    Chapter 8 Stability I: Equilibrium Points Suppose the system x_ = f(x); x 2 Rn (8.1) possesses an equilibrium point q i.e., f(q) = 0. Then x = q is a solution for all t. It is often important to know whether this solution is stable, i.e., whether it persists essentially unchanged on the infinite interval [0; 1) under small changes in the initial data. This is particularly important in applica- tions, where the initial data are often known imperfectly. Below we give a precise definition of stability for equilibrium solutions of systems of differen- tial equations, and this chapter is devoted to this subject. The system 8.1 is autonomous, i.e., the vector function f has no explicit dependence on the independent variable. We restrict consideration to Lyapunov stability, wherein only perturba- tions of the initial data are contemplated, and thereby exclude consideration of structural stability, in which one considers perturbations of the vector field (cf. [5] for a discussion of structural stability). 8.1 Lyapunov Stability Consider the system (8.1). We assume that f is in C1(Ω) where Ω is a domain in Rn. We denote by x = φ(t; p) the solution of equation (8.1) taking the value p when t = 0, noting that in the autonomous case there is no loss of generality in taking the initial instant t0 = 0 (see Problem 2 of Problem Set 6.4.1 above). 171 172 Definition 8.1.1 The equilibrium point q is said to be stable if given > 0 there is a δ > 0 such that kφ(t; p) − qk < for all t > 0 and for all p such that kp − qk < δ.
    [Show full text]