Van der Pol oscillator
About: Van der Pol oscillator is a(n) research topic. Over the lifetime, 3335 publication(s) have been published within this topic receiving 54257 citation(s).
01 Jul 1961-Biophysical Journal
TL;DR: Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle, which qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve.
Abstract: Van der Pol's equation for a relaxation oscillator is generalized by the addition of terms to produce a pair of non-linear differential equations with either a stable singular point or a limit cycle. The resulting "BVP model" has two variables of state, representing excitability and refractoriness, and qualitatively resembles Bonhoeffer's theoretical model for the iron wire model of nerve. This BVP model serves as a simple representative of a class of excitable-oscillatory systems including the Hodgkin-Huxley (HH) model of the squid giant axon. The BVP phase plane can be divided into regions corresponding to the physiological states of nerve fiber (resting, active, refractory, enhanced, depressed, etc.) to form a "physiological state diagram," with the help of which many physiological phenomena can be summarized. A properly chosen projection from the 4-dimensional HH phase space onto a plane produces a similar diagram which shows the underlying relationship between the two models. Impulse trains occur in the BVP and HH models for a range of constant applied currents which make the singular point representing the resting state unstable.
06 Jan 1994-
TL;DR: The phenomenology of chaos Towards a theory of nonlinear dynamics and chaos Quantifying chaos Special topics Appendices Index
Abstract: First Edition Preface First Edition Acknowledgments Second Edition Preface Second Edition Acknowledgments I. THE PHENOMENOLOGY OF CHAOS 1. Three Chaotic Systems 2. The Universality of Chaos II. TOWARDS A THEORY OF NONLINEAR DYNAMICS AND CHAOS 3. Dynamics in State Space: One and Two Dimensions 4. Three-Dimensional State Space and Chaos 5. Iterated Maps 6. Quasi-Periodicity and Chaos 7. Intermittency and Crises 8. Hamiltonian Systems III.MEASURES OF CHAOS 9. Quantifying Chaos 10. Many Dimensions and Multifractals IV.SPECIAL TOPICS 11. Pattern Formation and Spatiotemporal Chaos 12. Quantum Chaos, The Theory of Complexity, and other Topics Appendix A: Fourier Power Spectra Appendix B: Bifurcation Theory Appendix C: The Lorenz Model Appendix D: The Research Literature on Chaos Appendix E: Computer Programs Appendix F: Theory of the Universal Feigenbaum Numbers Appendix G: The Duffing Double-Well Oscillator Appendix H: Other Universal Feature for One-Dimensional Iterated Maps Appendix I: The van der Pol Oscillator Appendix J: Simple Laser Dynamics Models References Bibliography Index
01 Jan 1995-
Abstract: 1 Finite-Difference Equations.- 1.1 A Mythical Field.- 1.2 The Linear Finite-Difference Equation.- 1.3 Methods of Iteration.- 1.4 Nonlinear Finite-Difference Equations.- 1.5 Steady States and Their Stability.- 1.6 Cycles and Their Stability.- 1.7 Chaos.- 1.8 Quasiperiodicity.- 1 Chaos in Periodically Stimulated Heart Cells.- Sources and Notes.- Exercises.- Computer Projects.- 2 Boolean Networks and Cellular Automata.- 2.1 Elements and Networks.- 2.2 Boolean Variables, Functions, and Networks.- 2 A Lambda Bacteriophage Model.- 3 Locomotion in Salamanders.- 2.3 Boolean Functions and Biochemistry.- 2.4 Random Boolean Networks.- 2.5 Cellular Automata.- 4 Spiral Waves in Chemistry and Biology.- 2.6 Advanced Topic: Evolution and Computation.- Sources and Notes.- Exercises.- Computer Projects.- 3 Self-Similarity and Fractal Geometry.- 3.1 Describing a Tree.- 3.2 Fractals.- 3.3 Dimension.- 5 The Box-Counting Dimension.- 3.4 Statistical Self-Similarity.- 6 Self-Similarity in Time.- 3.5 Fractals and Dynamics.- 7 Random Walks and Levy Walks.- 8 Fractal Growth.- Sources and Notes.- Exercises.- Computer Projects.- 4 One-Dimensional Differential Equations.- 4.1 Basic Definitions.- 4.2 Growth and Decay.- 9 Traffic on the Internet.- 10 Open Time Histograms in Patch Clamp Experiments.- 11 Gompertz Growth of Tumors.- 4.3 Multiple Fixed Points.- 4.4 Geometrical Analysis of One-Dimensional Nonlinear Ordinary Differential Equations.- 4.5 Algebraic Analysis of Fixed Points.- 4.6 Differential Equations versus Finite-Difference Equations.- 4.7 Differential Equations with Inputs.- 12 Heart Rate Response to Sinusoid Inputs.- 4.8 Advanced Topic: Time Delays and Chaos.- 13 Nicholson's Blowflies.- Sources and Notes.- Exercises.- Computer Projects.- 5 Two-Dimensional Differential Equations.- 5.1 The Harmonic Oscillator.- 5.2 Solutions, Trajectories, and Flows.- 5.3 The Two-Dimensional Linear Ordinary Differential Equation.- 5.4 Coupled First-Order Linear Equations.- 14 Metastasis of Malignant Tumors.- 5.5 The Phase Plane.- 5.6 Local Stability Analysis of Two-Dimensional, Nonlinear Differential Equations.- 5.7 Limit Cycles and the van der Pol Oscillator.- 5.8 Finding Solutions to Nonlinear Differential Equations.- 15 Action Potentials in Nerve Cells.- 5.9 Advanced Topic: Dynamics in Three or More Dimensions.- 5.10 Advanced Topic: Poincare Index Theorem.- Sources and Notes.- Exercises.- Computer Projects.- 6 Time-Series Analysis.- 6.1 Starting with Data.- 6.2 Dynamics, Measurements, and Noise.- 16 Fluctuations in Marine Populations.- 6.3 The Mean and Standard Deviation.- 6.4 Linear Correlations.- 6.5 Power Spectrum Analysis.- 17 Daily Oscillations in Zooplankton.- 6.6 Nonlinear Dynamics and Data Analysis.- 18 Reconstructing Nerve Cell Dynamics.- 6.7 Characterizing Chaos.- 19 Predicting the Next Ice Age.- 6.8 Detecting Chaos and Nonlinearity.- 6.9 Algorithms and Answers.- Sources and Notes.- Exercises.- Computer Projects.- Appendix A A Multi-Functional Appendix.- A.1 The Straight Line.- A.2 The Quadratic Function.- A.3 The Cubic and Higher-Order Polynomials.- A.4 The Exponential Function.- A.5 Sigmoidal Functions.- A.6 The Sine and Cosine Functions.- A.7 The Gaussian (or "Normal") Distribution.- A.8 The Ellipse.- A.9 The Hyperbola.- Exercises.- Appendix B A Note on Computer Notation.- Solutions to Selected Exercises.
01 Feb 2004-Journal of Fluids and Structures
Abstract: A class of low-order models for vortex-induced vibrations is analyzed. A classical van der Pol equation models the near wake dynamics describing the fluctuating nature of vortex shedding. This wake oscillator interacts with the equation of motion of a one degree-of-freedom structural oscillator and several types of linear coupling terms modelling the fluid–structure interaction are considered. The model dynamics is investigated analytically and discussed with regard to the choice of the coupling terms and the values of model parameters. Closed-form relations of the model response are derived and compared to experimental results on forced and free vortex-induced vibrations. This allows us to set the values of all model parameters, then leads to the choice of the most appropriate coupling model. A linear inertia force acting on the fluid is thus found to describe most of the features of vortex-induced vibration phenomenology, such as Griffin plots and lock-in domains.
01 Aug 2001-Automatica
TL;DR: A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation, and extends the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data.
Abstract: A direct adaptive output feedback control design procedure is developed for highly uncertain nonlinear systems, that does not rely on state estimation. The approach is also applicable to systems of unknown, but bounded dimension. In particular, we consider single-input/single-output nonlinear systems, whose output has known, but otherwise arbitrary relative degree. This includes systems with both parameter uncertainty and unmodeled dynamics. The result is achieved by extending the universal function approximation property of linearly parameterized neural networks to model unknown system dynamics from input/output data. The network weight adaptation rule is derived from Lyapunov stability analysis, and guarantees that the adapted weight errors and the tracking error are bounded. Numerical simulations of an output feedback controlled van der Pol oscillator, coupled with a linear oscillator, is used to illustrate the practical potential of the theoretical results.