Hidden attractor

Last updated

In the bifurcation theory, a bounded oscillation that is born without loss of stability of stationary set is called a hidden oscillation. In nonlinear control theory, the birth of a hidden oscillation in a time-invariant control system with bounded states means crossing a boundary, in the domain of the parameters, where local stability of the stationary states implies global stability (see, e.g. Kalman's conjecture). If a hidden oscillation (or a set of such hidden oscillations filling a compact subset of the phase space of the dynamical system) attracts all nearby oscillations, then it is called a hidden attractor. For a dynamical system with a unique equilibrium point that is globally attractive, the birth of a hidden attractor corresponds to a qualitative change in behaviour from monostability to bi-stability. In the general case, a dynamical system may turn out to be multistable and have coexisting local attractors in the phase space. While trivial attractors, i.e. stable equilibrium points, can be easily found analytically or numerically, the search of periodic and chaotic attractors can turn out to be a challenging problem (see, e.g. the second part of Hilbert's 16th problem).

Contents

Classification of attractors as being hidden or self-excited

To identify a local attractor in a physical or numerical experiment, one needs to choose an initial system’s state in attractor’s basin of attraction and observe how the system’s state, starting from this initial state, after a transient process, visualizes the attractor. The classification of attractors as being hidden or self-excited reflects the difficulties of revealing basins of attraction and searching for the local attractors in the phase space.

Definition . [1] [2] [3] An attractor is called a hidden attractor if its basin of attraction does not intersect with a certain open neighbourhood of equilibrium points; otherwise it is called a self-excited attractor.

The classification of attractors as being hidden or self-excited was introduced by G. Leonov and N. Kuznetsov in connection with the discovery of the hidden Chua attractor [4] [5] [6] [7] for the first time in 2009 year. Similarly, an arbitrary bounded oscillation, not necessarily having an open neighborhood as the basin of attraction in the phase space, is classified as a self-excited or hidden oscillation.

Chaotic self-excited attractor (green domain) in Chua's system. Trajectories with initial data in neighborhoods of two saddle points (blue) and zero equilibrium point (orange) tend (green) to attractor. Self-Excited attractor in Chua circuit.jpg
Chaotic self-excited attractor (green domain) in Chua's system. Trajectories with initial data in neighborhoods of two saddle points (blue) and zero equilibrium point (orange) tend (green) to attractor.
Chaotic hidden attractor (green domain) in Chua's system. Trajectories with initial data in neighborhoods of two saddle points (blue) tend (red arrow) to infinity or tend (black arrow) to stable zero equilibrium point (orange). Chua-chaotic-hidden-attractor.jpg
Chaotic hidden attractor (green domain) in Chua's system. Trajectories with initial data in neighborhoods of two saddle points (blue) tend (red arrow) to infinity or tend (black arrow) to stable zero equilibrium point (orange).
Two hidden chaotic attractors and one hidden periodic attractor coexist with two trivial attractors in Chua circuit (from the IJBC cover ) Chua-hidden-attractors-3.jpg
Two hidden chaotic attractors and one hidden periodic attractor coexist with two trivial attractors in Chua circuit (from the IJBC cover )


Self-excited attractors

For a self-excited attractor, its basin of attraction is connected with an unstable equilibrium and, therefore, the self-excited attractors can be found numerically by a standard computational procedure in which after a transient process, a trajectory, starting in a neighbourhood of an unstable equilibrium, is attracted to the state of oscillation and then traces it (see, e.g. self-oscillation process). Thus, self-excited attractors, even coexisting in the case of multistability, can be easily revealed and visualized numerically. In the Lorenz system, for classical parameters, the attractor is self-excited with respect to all existing equilibria, and can be visualized by any trajectory from their vicinities; however, for some other parameter values there are two trivial attractors coexisting with a chaotic attractor, which is a self-excited one with respect to the zero equilibrium only. Classical attractors in Van der Pol, Beluosov–Zhabotinsky, Rössler, Chua, Hénon dynamical systems are self-excited.

A conjecture is that the Lyapunov dimension of a self-excited attractor does not exceed the Lyapunov dimension of one of the unstable equilibria, the unstable manifold of which intersects with the basin of attraction and visualizes the attractor. [8]

Hidden attractors

Hidden attractors have basins of attraction which are not connected with equilibria and are “hidden” somewhere in the phase space. For example, the hidden attractors are attractors in the systems without equilibria: e.g. rotating electromechanical dynamical systems with Sommerfeld effect (1902), in the systems with only one equilibrium, which is stable: e.g. counterexamples to the Aizerman's conjecture (1949) and Kalman's conjecture (1957) on the monostability of nonlinear control systems. One of the first related theoretical problems is the second part of Hilbert's 16th problem on the number and mutual disposition of limit cycles in two-dimensional polynomial systems where the nested stable limit cycles are hidden periodic attractors. The notion of a hidden attractor has become a catalyst for the discovery of hidden attractors in many applied dynamical models. [1] [9] [10]

In general, the problem with hidden attractors is that there are no general straightforward methods to trace or predict such states for the system’s dynamics (see, e.g. [11] ). While for two-dimensional systems, hidden oscillations can be investigated using analytical methods (see, e.g., the results on the second part of Hilbert's 16th problem), for the study of stability and oscillations in complex nonlinear multidimensional systems, numerical methods are often used. In the multi-dimensional case the integration of trajectories with random initial data is unlikely to provide a localization of a hidden attractor, since a basin of attraction may be very small, and the attractor dimension itself may be much less than the dimension of the considered system. Therefore, for the numerical localization of hidden attractors in multi-dimensional space, it is necessary to develop special analytical-numerical computational procedures, [1] [12] [8] which allow one to choose initial data in the attraction domain of the hidden oscillation (which does not contain neighborhoods of equilibria), and then to perform trajectory computation. There are corresponding effective methods based on homotopy and numerical continuation: a sequence of similar systems is constructed, such that for the first (starting) system, the initial data for numerical computation of an oscillating solution (starting oscillation) can be obtained analytically, and then the transformation of this starting oscillation in the transition from one system to another is followed numerically.

Theory of hidden oscillations

Afraimovich Award granted to N. Kuznetsov for The theory of hidden oscillations and stability of dynamical systems (2021) Afraimovich Award Diploma 2021.jpg
Afraimovich Award granted to N. Kuznetsov for The theory of hidden oscillations and stability of dynamical systems (2021)

The classification of attractors as self-exited or hidden ones was a fundamental premise for the emergence of the theory of hidden oscillations, which represents the modern development of Andronov’s theory of oscillations. It is key to determining the exact boundaries of the global stability, parts of which are classified by N. Kuznetsov as trivial (i.e., determined by local bifurcations) or as hidden (i.e., determined by non-local bifurcations and by the birth of hidden oscillations). [13] [14]

Related Research Articles

<span class="mw-page-title-main">Lyapunov exponent</span> The rate of separation of infinitesimally close trajectories

In mathematics, the Lyapunov exponent or Lyapunov characteristic exponent of a dynamical system is a quantity that characterizes the rate of separation of infinitesimally close trajectories. Quantitatively, two trajectories in phase space with initial separation vector diverge at a rate given by

<span class="mw-page-title-main">Attractor</span> Concept in dynamical systems

In the mathematical field of dynamical systems, an attractor is a set of states toward which a system tends to evolve, for a wide variety of starting conditions of the system. System values that get close enough to the attractor values remain close even if slightly disturbed.

Hilbert's 16th problem was posed by David Hilbert at the Paris conference of the International Congress of Mathematicians in 1900, as part of his list of 23 problems in mathematics.

<span class="mw-page-title-main">Rabinovich–Fabrikant equations</span>

The Rabinovich–Fabrikant equations are a set of three coupled ordinary differential equations exhibiting chaotic behaviour for certain values of the parameters. They are named after Mikhail Rabinovich and Anatoly Fabrikant, who described them in 1979.

<span class="mw-page-title-main">Bifurcation theory</span> Study of sudden qualitative behavior changes caused by small parameter changes

Bifurcation theory is the mathematical study of changes in the qualitative or topological structure of a given family of curves, such as the integral curves of a family of vector fields, and the solutions of a family of differential equations. Most commonly applied to the mathematical study of dynamical systems, a bifurcation occurs when a small smooth change made to the parameter values of a system causes a sudden 'qualitative' or topological change in its behavior. Bifurcations occur in both continuous systems and discrete systems.

Nonlinear control theory is the area of control theory which deals with systems that are nonlinear, time-variant, or both. Control theory is an interdisciplinary branch of engineering and mathematics that is concerned with the behavior of dynamical systems with inputs, and how to modify the output by changes in the input using feedback, feedforward, or signal filtering. The system to be controlled is called the "plant". One way to make the output of a system follow a desired reference signal is to compare the output of the plant to the desired output, and provide feedback to the plant to modify the output to bring it closer to the desired output.

<span class="mw-page-title-main">Chua's circuit</span>

Chua's circuit is a simple electronic circuit that exhibits classic chaotic behavior. This means roughly that it is a "nonperiodic oscillator"; it produces an oscillating waveform that, unlike an ordinary electronic oscillator, never "repeats". It was invented in 1983 by Leon O. Chua, who was a visitor at Waseda University in Japan at that time. The ease of construction of the circuit has made it a ubiquitous real-world example of a chaotic system, leading some to declare it "a paradigm for chaos".

In control systems theory, the describing function (DF) method, developed by Nikolay Mitrofanovich Krylov and Nikolay Bogoliubov in the 1930s, and extended by Ralph Kochenburger is an approximate procedure for analyzing certain nonlinear control problems. It is based on quasi-linearization, which is the approximation of the non-linear system under investigation by a linear time-invariant (LTI) transfer function that depends on the amplitude of the input waveform. By definition, a transfer function of a true LTI system cannot depend on the amplitude of the input function because an LTI system is linear. Thus, this dependence on amplitude generates a family of linear systems that are combined in an attempt to capture salient features of the non-linear system behavior. The describing function is one of the few widely applicable methods for designing nonlinear systems, and is very widely used as a standard mathematical tool for analyzing limit cycles in closed-loop controllers, such as industrial process controls, servomechanisms, and electronic oscillators.

<span class="mw-page-title-main">Lorenz system</span> System of ordinary differential equations with chaotic solutions

The Lorenz system is a system of ordinary differential equations first studied by mathematician and meteorologist Edward Lorenz. It is notable for having chaotic solutions for certain parameter values and initial conditions. In particular, the Lorenz attractor is a set of chaotic solutions of the Lorenz system. In popular media the "butterfly effect" stems from the real-world implications of the Lorenz attractor, namely that several different initial chaotic conditions evolve in phase space in a way that never repeats, so all chaos is unpredictable. This underscores that chaotic systems can be completely deterministic and yet still be inherently unpredictable over long periods of time. Because chaos continually increases in systems, we cannot predict the future of systems well. E.g., even the small flap of a butterfly’s wings could set the world on a vastly different trajectory, such as by causing a hurricane. The shape of the Lorenz attractor itself, when plotted in phase space, may also be seen to resemble a butterfly.

In mathematics, the Markus–Yamabe conjecture is a conjecture on global asymptotic stability. If the Jacobian matrix of a dynamical system at a fixed point is Hurwitz, then the fixed point is asymptotically stable. Markus-Yamabe conjecture asks if a similar result holds globally. Precisely, the conjecture states that if a continuously differentiable map on an -dimensional real vector space has a fixed point, and its Jacobian matrix is everywhere Hurwitz, then the fixed point is globally stable.

In nonlinear control, Aizerman's conjecture or Aizerman problem states that a linear system in feedback with a sector nonlinearity would be stable if the linear system is stable for any linear gain of the sector. This conjecture was proven false but led to the (valid) sufficient criteria on absolute stability.

<span class="mw-page-title-main">Stellar pulsation</span>

Stellar pulsations are caused by expansions and contractions in the outer layers as a star seeks to maintain equilibrium. These fluctuations in stellar radius cause corresponding changes in the luminosity of the star. Astronomers are able to deduce this mechanism by measuring the spectrum and observing the Doppler effect. Many intrinsic variable stars that pulsate with large amplitudes, such as the classical Cepheids, RR Lyrae stars and large-amplitude Delta Scuti stars show regular light curves.

<span class="mw-page-title-main">Multiscroll attractor</span> Strange attractor connected by two 2-dimensional rings

In the mathematics of dynamical systems, the double-scroll attractor is a strange attractor observed from a physical electronic chaotic circuit with a single nonlinear resistor. The double-scroll system is often described by a system of three nonlinear ordinary differential equations and a 3-segment piecewise-linear equation. This makes the system easily simulated numerically and easily manifested physically due to Chua's circuits' simple design.

Kalman's conjecture or Kalman problem is a disproved conjecture on absolute stability of nonlinear control system with one scalar nonlinearity, which belongs to the sector of linear stability. Kalman's conjecture is a strengthening of Aizerman's conjecture and is a special case of Markus–Yamabe conjecture. This conjecture was proven false but led to the (valid) sufficient criteria on absolute stability.

In the mathematics of dynamical systems, Eden's conjecture states that the supremum of the local Lyapunov dimensions on the global attractor is achieved on a stationary point or an unstable periodic orbit embedded into the attractor. The validity of the conjecture was proved for a number of well-known systems having global attractor. It is named after Alp Eden, who proposed it in 1987.

In the mathematics of dynamical systems, the concept of Lyapunov dimension was suggested by Kaplan and Yorke for estimating the Hausdorff dimension of attractors. Further the concept has been developed and rigorously justified in a number of papers, and nowadays various different approaches to the definition of Lyapunov dimension are used. Remark that the attractors with noninteger Hausdorff dimension are called strange attractors. Since the direct numerical computation of the Hausdorff dimension of attractors is often a problem of high numerical complexity, estimations via the Lyapunov dimension became widely spread. The Lyapunov dimension was named after the Russian mathematician Aleksandr Lyapunov because of the close connection with the Lyapunov exponents.

Nikolay Vladimirovich Kuznetsov is a specialist in nonlinear dynamics and control theory.

In mechanics, Sommerfeld effect is a phenomenon arising from feedback in the energy exchange between vibrating systems: for example, when for the rocking table, under given conditions, energy transmitted to the motor resulted not in higher revolutions but in stronger vibrations of the table. It is named after Arnold Sommerfeld. In 1902, A. Sommerfeld analyzed the vibrations caused by a motor driving an unbalanced weight and wrote that "This experiment corresponds roughly to the case in which a factory owner has a machine set on a poor foundation running at 30 horsepower. He achieves an effective level of just 1/3, however, because only 10 horsepower are doing useful work, while 20 horsepower are transferred to the foundational masonry". First mathematical descriptions of Sommerfeld effect were suggested by I. Blekhman and V. Konenko.

William F. Egan was well-known expert and author in the area of PLLs. The first and second editions of his book Frequency Synthesis by Phase Lock as well as his book Phase-Lock Basics are references among electrical engineers specializing in areas involving PLLs.

References

  1. 1 2 3 Leonov G.A.; Kuznetsov N.V. (2013). "Hidden attractors in dynamical systems. From hidden oscillations in Hilbert-Kolmogorov, Aizerman, and Kalman problems to hidden chaotic attractor in Chua circuits". International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. 23 (1): 1330002–219. Bibcode:2013IJBC...2330002L. doi:10.1142/S0218127413300024.
  2. Bragin V.O.; Vagaitsev V.I.; Kuznetsov N.V.; Leonov G.A. (2011). "Algorithms for Finding Hidden Oscillations in Nonlinear Systems. The Aizerman and Kalman Conjectures and Chua's Circuits" (PDF). Journal of Computer and Systems Sciences International. 50 (5): 511–543. doi:10.1134/S106423071104006X. S2CID   21657305.
  3. Leonov, G.A.; Kuznetsov, N.V.; Mokaev, T.N. (2015). "Homoclinic orbits, and self-excited and hidden attractors in a Lorenz-like system describing convective fluid motion". The European Physical Journal Special Topics. 224 (8): 1421–1458. arXiv: 1505.04729 . doi:10.1140/epjst/e2015-02470-3. S2CID   119227870.
  4. Kuznetsov N.V.; Leonov G.A.; Vagaitsev V.I. (2010). "Analytical-numerical method for attractor localization of generalized Chua's system". IFAC Proceedings Volumes. 43 (11): 29–33. doi: 10.3182/20100826-3-TR-4016.00009 .
  5. Leonov G.A.; Vagaitsev V.I.; Kuznetsov N.V. (2011). "Localization of hidden Chua's attractors" (PDF). Physics Letters. 375 (23): 2230–2233. Bibcode:2011PhLA..375.2230L. doi:10.1016/j.physleta.2011.04.037.
  6. Leonov G.A.; Vagaitsev V.I.; Kuznetsov N.V. (2012). "Hidden attractor in smooth Chua systems" (PDF). Physica D. 241 (18): 1482–1486. Bibcode:2012PhyD..241.1482L. doi:10.1016/j.physd.2012.05.016.
  7. 1 2 Stankevich N. V.; Kuznetsov N. V.; Leonov G. A.; Chua L. (2017). "Scenario of the birth of hidden attractors in the Chua circuit". International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. 27 (12): 1730038–188. arXiv: 1710.02677 . Bibcode:2017IJBC...2730038S. doi:10.1142/S0218127417300385. S2CID   45604334.
  8. 1 2 Kuznetsov, N.V.; Leonov, G.A.; Mokaev, T.N.; Prasad, A.; Shrimali, M.D. (2018). "Finite-time Lyapunov dimension and hidden attractor of the Rabinovich system". Nonlinear Dynamics. 92 (2): 267–285. arXiv: 1504.04723 . doi:10.1007/s11071-018-4054-z. S2CID   54706479.
  9. Kuznetsov N. V.; Leonov G. A. (2014). "Hidden attractors in dynamical systems: systems with no equilibria, multistability and coexisting attractors". IFAC Proceedings Volumes (IFAC World Congress Proceedings). 47 (3): 5445–5454. doi:10.3182/20140824-6-ZA-1003.02501.
  10. Dudkowski D.; Jafari S.; Kapitaniak T.; Kuznetsov N. V.; Leonov G. A.; Prasad A. (2016). "Hidden attractors in dynamical systems". Physics Reports. 637: 1–50. Bibcode:2016PhR...637....1D. doi:10.1016/j.physrep.2016.05.002.
  11. Kuznetsov, N.V.; Leonov, G.A.; Yuldashev, M.V.; Yuldashev, R.V. (2017). "Hidden attractors in dynamical models of phase-locked loop circuits: limitations of simulation in MATLAB and SPICE". Communications in Nonlinear Science and Numerical Simulation. 51: 39–49. Bibcode:2017CNSNS..51...39K. doi:10.1016/j.cnsns.2017.03.010.
  12. Chen, G.; Kuznetsov, N.V.; Leonov, G.A.; Mokaev, T.N. (2015). "Hidden attractors on one path: Glukhovsky-Dolzhansky, Lorenz, and Rabinovich systems". International Journal of Bifurcation and Chaos in Applied Sciences and Engineering. 27 (8): art. num. 1750115. arXiv: 1705.06183 . doi:10.1142/S0218127417501152. S2CID   21425647.
  13. Kuznetsov N.V. (2020). "Theory of hidden oscillations and stability of control systems" (PDF). Journal of Computer and Systems Sciences International. 59 (5): 647–668. doi:10.1134/S1064230720050093. S2CID   225304463.
  14. Kuznetsov, N.V.; Mokaev, T.N.; Kuznetsova, O.A.; Kudryashova, E.V. (2020). "The Lorenz system: hidden boundary of practical stability and the Lyapunov dimension". Nonlinear Dynamics. 102 (2): 713–732. doi: 10.1007/s11071-020-05856-4 .

Books

Selected lectures