Periodic Perturbations and Three-Branch Return Maps of an Oscillatory Chemical System

1995 ◽  
Vol 99 (45) ◽  
pp. 16636-16640 ◽  
Author(s):  
Andrzej Lech Kawczynski ◽  
Kedma Bar-Eli
1994 ◽  
Vol 98 (47) ◽  
pp. 12248-12254 ◽  
Author(s):  
Gregory Markman ◽  
Kedma Bar-Eli

2003 ◽  
Vol 2003 (31) ◽  
pp. 1981-1991 ◽  
Author(s):  
Malay Bandyopadhyay ◽  
Rakhi Bhattacharya ◽  
C. G. Chakrabarti

The present paper dealing with the nonlinear bifurcation analysis of two-species oscillatory system consists of three parts. The first part deals with Hopf-bifurcation and limit cycle analysis of the homogeneous system. The second consists of travelling wave train solution and its linear stability analysis of the system in presence of diffusion. The last deals with an oscillatory chemical system as an illustrative example.


Author(s):  
Ricardo Aguilar López ◽  
Rafael Martínez Guerra ◽  
Juan L. Mata Machuca

The aim of this paper is to present the synthesis of a robust control law for the control of a class of nonlinear systems named Liouvillian. The control design is based on a sliding-mode uncertainty estimator developed under the framework of algebraic-differential concepts. The estimation convergence is done by the Lyapunov-type analysis and the closed-loop system stability is shown by means of the regulation error dynamics. Robustness of the proposed control scheme is tested in the face of noise output measurements and model uncertainties. The performance of the proposed control law is illustrated with numerical simulations in which a class of oscillatory chemical system is used as application example.


Author(s):  
Irving R. Epstein ◽  
John A. Pojman

There is no doubt that the phenomenon of chemical oscillation—the periodic or nearly periodic temporal variation of concentrations in a reacting system— provided the initial impetus for the development of nonlinear chemical dynamics, and has continued to be the most thoroughly studied of the phenomena that constitute this rich field. In our opening chapter, we alluded to the early skepticism that experimental observations of chemical oscillation engendered. We also noted that the first chemical oscillators were discovered accidentally, by researchers looking for other phenomena. It is relatively easy to understand intuitively why a typical physical oscillator, like a spring, should behave in a periodic fashion. It is considerably more difficult for most of us to see how a chemical reaction might undergo oscillation. As a result, the thought of building a physical oscillator seems far more reasonable than the notion of designing an oscillatory chemical reaction. In this chapter, we will examine how chemical oscillation can arise, in general, and how it is possible to create chemical reactions that are likely to show oscillatory behavior. In the next chapter, we will discuss how to take a chemical oscillator apart and analyze why it oscillates—the question of mechanism. We also look in detail there at the mechanisms of several oscillating reactions. In order to gain some insight into how oscillation might arise in a chemical system, we shall consider a very simple and general model for a reaction involving two concentrations, u and v. Two independent concentration variables is the smallest number that can generate oscillatory behavior in a chemical system. The basic idea, however, is applicable to many-variable systems, because the essential features of the dynamics are often controlled by a small number of variables, and the other variables simply follow the behavior of the key species.


2003 ◽  
Vol 119 (6) ◽  
pp. 3291-3296 ◽  
Author(s):  
Harold M. Hastings ◽  
Richard J. Field ◽  
Sabrina G. Sobel

1992 ◽  
Vol 96 (22) ◽  
pp. 8898-8904 ◽  
Author(s):  
Yi Xue Zhang ◽  
Petra Foerster ◽  
John Ross

2015 ◽  
Vol 89 (13) ◽  
pp. 2349-2358 ◽  
Author(s):  
S. N. Blagojević ◽  
Ž. Čupić ◽  
A. Ivanović-Šašić ◽  
Lj. Kolar-Anić

1994 ◽  
Vol 33 (01) ◽  
pp. 81-84 ◽  
Author(s):  
S. Cerutti ◽  
S. Guzzetti ◽  
R. Parola ◽  
M.G. Signorini

Abstract:Long-term regulation of beat-to-beat variability involves several different kinds of controls. A linear approach performed by parametric models enhances the short-term regulation of the autonomic nervous system. Some non-linear long-term regulation can be assessed by the chaotic deterministic approach applied to the beat-to-beat variability of the discrete RR-interval series, extracted from the ECG. For chaotic deterministic systems, trajectories of the state vector describe a strange attractor characterized by a fractal of dimension D. Signals are supposed to be generated by a deterministic and finite dimensional but non-linear dynamic system with trajectories in a multi-dimensional space-state. We estimated the fractal dimension through the Grassberger and Procaccia algorithm and Self-Similarity approaches of the 24-h heart-rate variability (HRV) signal in different physiological and pathological conditions such as severe heart failure, or after heart transplantation. State-space representations through Return Maps are also obtained. Differences between physiological and pathological cases have been assessed and generally a decrease in the system complexity is correlated to pathological conditions.


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