Research Article | Open Access
Time-Delayed Interactions in Networks of Self-Adapting Hopf Oscillators
A network of coupled limit cycle oscillators with delayed interactions is considered. The parameters characterizing the oscillator’s frequency and limit cycle are allowed to self-adapt. Adaptation is due to time-delayed state variables that mutually interact via a network. The self-adaptive mechanisms ultimately drive all coupled oscillators to a consensual cyclostationary state, where the values of the parameters are identical for all local systems. They are analytically expressible. The interplay between the spectral properties of the coupling matrix and the time delays determines the conditions for which convergence towards a consensual state takes place. Once reached, this consensual state subsists even if interactions are removed. In our class of models, the consensual values of the parameters depend neither on the delays nor on the network’s topologies.
The farther back you can look, the farther forward you are likely to see
The harmonic excitation of an elementary-damped harmonic oscillator
with , , and produces the well-known asymptotic response (c.f. ):
where and depend on the control parameters ,
In our present paper, we will extend the previous classical system-environment relationship in order to allow more realistic situations where mutual interactions permanently affect both the system and the environment. Such adaptive mechanisms can modify individual dynamics on a permanent basis. To stylize this new situation, the basic dynamics given by (3) is modified as
The particular case of (5) when (i.e., for all ) has been studied in [4, 5]. This type of dynamical system provides a cornerstone of bioinspired robotics where legs (or arms) of robots may be modeled by damped oscillators as (1) (i.e., ). To ensure a maximum leg stride, the damped oscillators must be excited by at the damped oscillator's resonant frequency (i.e.,
The case where neither nor is trivial has been covered in [7, 8], where the authors not only considered two adaptive coupled limit cycle oscillators, but mutually interacting through a network. Here, self-adapting oscillators can be applied to robot formation modeling. Each individual robot belonging to a swarm, circulating around a specific point, adapts its angular velocity in order to lower the amount of exchanged information to maintain the formation. Self-adaptation in networks is also considered in , where here the control signals (and not the local systems) of the variables of the CPG adapt, and this, to quickly react to new situations and produce several different behavioral patterns.
Building on what has been done in previous contributions [4, 5, 7, 8], we here consider a network of limit cycle oscillators interaction with time-delayed state variables. The general form of our dynamical system in the phase-radius coordinates (i.e., polar coordinates and ) is
where and govern the local dynamics, and are the state variables, is a parameter set determining the local characteristics and are coupling strengths. The coupling dynamics is the gradient of a potential
This paper is organized as follows: in Section 2 we define the three components that together form the global system. We then discuss the dynamics of our model in Section 3. An application is presented in Section 4 which is then followed by some numerical experiments in Section 5. Finally, we conclude in Section 6.
2. Networks of Hopf Oscillators with Adaptive Mechanisms
We now present explicitly the local and coupling dynamics as well as the adaptive mechanisms on which we will focus.
2.1. Local Dynamics
Each node of the network is equipped with a local dynamical system. In this contribution, a local system is a Hopf oscillator presented here in its polar coordinates: The state variables are and are, for the time being, fixed and constant parameters. The parameter controls the frequency of the th oscillator given by the phase dynamics . The radial dynamics produces a stable circular limit cycle with radius .
2.2. Coupling Dynamics
Associated to an vertex connected and undirected network, denote by the weighted adjacency matrix with positive entries . Let be the corresponding Laplacian matrix (, where is the diagonal matrix with ). The coupling dynamics is given by the gradient of the positive semidefinite function with and and where the matrix has entries . This matrix is positive semidefinite since all its eigenvalues are positive (i.e. nonnegative). This is a direct application of Gershgorin’s circle theorem : for any eigenvalue of , there exists such that and so . In particular, the coupling dynamics is where are coupling strengths.
2.3. Adaptive Mechanisms
In this section, we now allow the fixed and constant parameters and to (I)become time dependent, that is, , for , (II)and each of them have their own dynamics, depending solely on the state variables and , that is,
Among the numerous variants for changing the values of the parameter, we focus on those presented in [7, 8, 12], that is,
where are the entries of and
3. Network's Dynamical System with Delay
We now discuss the resulting dynamics in the presence of a time delay affecting both the coupling dynamics and the adaptive mechanisms. We hence consider for . For (14), we have the following.
Two Constants of Motion. The functions
are constants of motion: in other words, if and are orbits of (14), then
Existence of a Consensual Oscillatory State. We can explicitly exhibit a consensual oscillatory state. Indeed, for given and ,
Observe that in absence of the radial component and without adaptation, (14) yields the famous Kuramoto model with delays [13, 14]. In these contributions, the authors considered the coupling dynamics with delay of the form:
In the absence of time delay (i.e.,
Convergence towards a Consensual Oscillatory State. The Limit (18) raises two issues: (1) the existence itself and (2) the limit values and . For expository reasons, we first discuss the limit values and then the convergence conditions.
Limit Values. Thanks to the constant of motions in (15), we have
with and orbits of (14) with given initial conditions. Supposing that Limit (18) holds, we hence have
and so the asymptotic values are analytically expressed as
It is important to emphasize that the consensual values and do not depend on the network topology (i.e., not on ), nor on the initial conditions of the state variables (i.e., not on nor on the time delay (i.e., not on
Convergence Conditions. To this aim we study the first-order approximation of (14) in the vicinity of Solution (17) and assume that linear stability analysis is sufficient to infer convergence conditions for the nonlinear system. Accordingly, we study the asymptotic behavior of the small perturbations , and and write Taking into account the constant of motions, we impose that
First Order Approximation. Rearranging the variables (i.e., the first are the , the second are the , the third are the , and finally the last are the ), the first-order approximation of (14) is
with the identity matrix , diagonal matrices
Diagonalization. Suppose now that
Changing the basis of System (24) with a bloc matrix (each bloc of size ) with
The case is worked out in the appendix. For , let us focus on the -dimensional systems and we rewrite (25a)-(25b) as linear second-order time delayed differential equations The convergence towards a consensual state is hence determined by the asymptotic stability of the zero solution of (26). Stability follows if, and only if, all roots of the corresponding characteristic equations have strictly negative real parts (c.f.  for details). For (25a), one can apply Theorem 3.3 in  which states that in this case the zero solution is asymptotically stable if and only if(I)(II)for . We emphasize that the consensual value influences the condition for convergence whereas it is not the case for . This leads to the idea that shaping the attractor is more delicate than tuning the angular velocity. This has been observed in .
Adaptation only on . If there is no adaptation on the radii (i.e.,
No Time Delay in the Coupling Dynamics. If there is no time delay in the coupling dynamics (i.e., the coupling dynamics is defined as in (11) with no delay), then (26) becomes, for , Invoking Theorem 3.5 in , the zero solution for (30a) and (30b) is asymptotically stable if and only if.
for (30b) for .
Summary. For a network (with arbitrary topology but with symmetric, positive entries adjacency matrix) of Hopf oscillators (as defined in Section 2.1 by (8)) interacting through time delayed Kuramoto type coupling (as defined in Section 2.2 by (11)) and with time-delay adaptive mechanisms (as defined in Section 2.3 by (13)) on the frequencies and amplitudes of the local systems—in other words, for (14), we have the following. (I)two constants of motions (c.f. (15)), (II)the existence of a consensual oscillatory state (c.f. (17)),(III)the consensual oscillatory state is linearly (i.e., locally) stable if all the roots of characteristic equations corresponding to (26) have strictly negative real parts, (IV)if there is no adaptation on the radii, the consensual oscillatory state is linearly (i.e., locally) stable if (27a), (27b), and (29) hold, (V)if there is no delay in the coupling dynamics, the consensual oscillatory state is linearly (i.e., locally) stable if (31), and (32) hold.
3.1. Miscellaneous Remark: Delayed Stabilization Mechanism
In this section we discuss the particular case arising when the delay is introduced only in the stabilization mechanism (i.e., dissipative part) of the local dynamics. The dynamical system is
Equations (33) still admit the existence of a consensual oscillatory state and two constants of motion as in (15) and in (17), respectively. Linear stability analysis of the consensual state reduces to the study of for . The zero solution for (34a) is asymptotically stable. For (34b), we apply Theorem in  which states that in this case the zero solution is asymptotically stable if and only if(I)(II) for .
Conceptually, the problem of reliably distributing time and frequency among several spatial remote locations is a “leitmotiv” in applications ranging from basic metrology, navigation and position determination, signal processing, computer communications, energy distribution networks, swarms robotics, bioengineering, multiagents systems, life sciences, acoustics, and musical art to give but only a highly nonexhaustive list. Presently, a strong research impetus is devoted to complex interacting oscillating systems able to exhibit self-adaptive capabilities leading to a resilient consensual dynamic. Whatever the configurations under study, communication delays between the collection of interacting subparts of the global systems are physically unavoidable. Depending on the underlying time scales, delays do strongly affect the resulting dynamics. Our class of models explicitly study the influence of delays and in particular their destabilizing effects, that modify the instantaneous behavior. By an appropriate tuning of control parameters (e.g., susceptibility constants), our class of models offer, via a unique formalism, the possibility to continuously explore interacting configurations ranging from slave-master (i.e., system-environment relationship) to fully decentralized regimes.
Alternatively, we may view this problematic in the context of soft-controlled systems which presently receive a sustain attention . Here, a swarm of agents is infiltrated by a lure agent (sometimes called a shill in economy). While the lure exhibits all the features of any ordinary agent, it can be externally controlled by an operator. As the interactions between the lure and any agent of the swarm remain unaffected (i.e., the lure remains incognito to ordinary agents), the external control of the lure can ultimately drive the whole population to a specific configuration. In our class of dynamics, a suitable choice of the susceptibility constant of a given local system (i.e., oscillator) may convert it into a shill. Indeed, in view of (21), the ultimate consensual values and are weighted averages. Such weighted averages can be made to be strongly dependent on a very insensitive shill-stubborn to any external influence (i.e., with a very low susceptibility constant).
In absence of time delays, convergence towards a consensual state is observed even for large heterogeneities (widely dispersed initial frequencies and radii and large discrepancies among the susceptibility constants). Hence, a shill agent can be easily introduced. However, our present study shows, that time delays restrict the conditions for convergence towards a consensual state. As a consequence, the implementation of a lure is more delicate matter (i.e., here, the dynamics is far more sensitive to the value taken by the susceptibility constants).
5. Numerical Simulations
Adaptation on and . We consider four Hopf oscillators interacting on a network with topology as in Figure 1. We choose the coupling strengths and susceptibility constants as
The resulting dynamics is shown in Figure 2. With the same initial conditions, we carry out another numerical simulation with here
Note that in Figure 2(a), the converge close to , that is, close to the initial value . This because the first oscillator’s susceptibility is “small” and hence it is this local system that acts as a shill. It interacts with its neighbor in the same way as they act with it. It can control the behavior of the network and this without being connected to all other local systems.
In the extreme case, when
Adaptation only on . Two Hopf oscillators, both having the same radius for the attractor (i.e.,
It is more the rule than the exception that parameter adaptation in dynamical systems can be achieved via delayed mechanisms. This therefore converts ordinary differential equations arising in absence of delay to functional differential equations. Stability issues become more difficult to discuss since, dealing with functional differential equations, an infinite number of degrees of freedom is introduced into the dynamics. For the class of oscillatory networks with parametric adaptation we here considered, we are able to observe how the time delay affects the adaptation mechanisms. While it is intuitively expected, that large delays are likely to destabilize the dynamics, we are here able to analytically quantify the underlying critical delays. The analytical linear stability discussion is made possible since, for our class of dynamics, stability issues can be reduced to the study of two linear second order functional differential equations for which suitable theorems can be found. Finally, we emphasize that numerical simulations show that adaptation may enhance the emergence of common dynamical pattern whereas in classical synchronization, time delays may be too large for synchronous motion to be attained.
Assuming that all perturbations and for converge to zero, let us now study the case for . Here, and so
and therefore and for all . Both of these constants and are zero. This is because the first orthonormal base vector (i.e., the normalized eigenvector for the eigenvalue ) is
M.-O. Hongler acknowledges partial support from the ESF (European Science Foundation) under the project entitled Exploring the Physics of Small Devices. J. Rodriguez acknowledges the support from the DFG-IRTG 1132 (Deutsche Forschungsgemeinschaft—International Research Training Group) under the project entitled Internationales Graduiertenkolleg—Stochastics and Real World Models.
- A. A. Shabana, Theory of Vibration: An Introduction, Mechanical Engineering Series, Springer, New York, NY, USA, 1996.
- M. Lakshmanan and S. Rajasekar, Nonlinear Dynamics: Integrability, Chaos and Patterns, Advanced Texts in Physics, Springer, New York, NY, USA, 2003.
- J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields, vol. 42 of Applied Mathematical Sciences, Springer, New York, NY, USA, 1990.
- J. Buchli, Ijspeert, and A. J. A. Simple, “Adaptive locomotion toy-system,” in Proceedings of the 8th International Conference on the Simulation of Adaptive Behavior (SAB'04), 2004.
- L. Righetti, J. Buchli, and A. J. Ijspeert, “Dynamic Hebbian learning in adaptive frequency oscillators,” Physica D, vol. 216, no. 2, pp. 269–281, 2006.
- R. Ronsse, N. Vitiello, T. Lenzi, J. van den Kieboom, M. C. Carrozza, and A. J. Ijspeert, “Human-robot synchrony: flexible assistance using adaptive oscillators,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 4, pp. 1001–1012, 2011.
- J. Rodriguez and M. O. Hongler, “Networks of mixed canonical-dissipative systems and dynamic hebbian learning,” International Journal of Computational Intelligence Systems, vol. 2, no. 2, pp. 140–146, 2009.
- J. Rodriguez and M. O. Hongler, “Networks of limit cycle oscillators with parametric learning capability,” in Recent Advances in Nonlinear Dynamics and Synchronization: Theory and Applications, K. Kyamakya, W. A. Halang, H. Unger, J. C. Chedjou, N. F. Rulkov, and Z. Li, Eds., pp. 17–48, Springer, Berlin, Germany, 2009.
- S. Steingrube, M. Timme, F. Wörgötter, and P. Manoonpong, “Self-organized adaptation of a simple neural circuit enables complex robot behaviour,” Nature Physics, vol. 6, no. 3, pp. 224–230, 2010.
- A. A. Selivanov, J. Lehnert, T. Dahms, P. Hövel, A. L. Fradkov, and E. Schöll, “Adaptive synchronization in delay-coupled networks of Stuart-Landau oscillators,” Physical Review E, vol. 85, no. 1, Article ID 016201, 8 pages, 2012.
- S. Gerschgorin, “Über die Abgrenzung der Eigenwerte einer Matrix,” IzvestIya AkademII Nauk USSR, OtdelenIe MatematIcheskIh I estestvennIh Nauk, vol. 7, pp. 749–754, 1931.
- J. Rodriguez, Networks of self-adaptive dynamical systems [Ph.D. thesis], Ecole Polythechnique Fédérale de Lausanne, 2011.
- M. K. S. Yeung and S. H. Strogatz, “Time delay in the Kuramoto model of coupled oscillators,” Physical Review Letters, vol. 82, no. 3, pp. 648–651, 1999.
- E. Montbrió, D. Pazó, and J. Schmidt, “Time delay in the Kuramoto model with bimodal frequency distribution,” Physical Review E, vol. 74, no. 5, Article ID 056201, 5 pages, 2006.
- R. Bellman and K. L. Cooke, Delay Differential Equations with Applications in Populations Dynamics, Academic Press, New York, NY, USA, 1963.
- B. Cahlon and D. Schmidt, “Stability criteria for certain second order delay differential equations,” Dynamics of Continuous, Discrete & Impulsive Systems, vol. 10, no. 4, pp. 593–621, 2003.
- J. Rodriguez, M. O. Hongler, and P. Blanchard, “Self-adaptive attractor-shaping for oscillators networks,” in Proceedings of the Joint INDS'11 & ISTET'11—3rd International Workshop on nonlinear Dynamics and Synchronization and 6th International Symposium on Theoretical Electrical Engineering, 2011.
- Y. Kuang, Delay Differential Equations with Applications in Population Dynamics, vol. 191 of Mathematics in Science and Engineering, Academic Press, San Diego, Calif, USA, 1993.
- J. Han, M. Li, and L. Guo, “Soft control on collective behavior of a group of autonomous agents by a shill agent,” Journal of Systems Science & Complexity, vol. 19, no. 1, pp. 54–62, 2006.
Copyright © 2013 Julio Rodriguez et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.