Book contents
- Frontmatter
- Contents
- Preface to the first edition
- Preface to the second edition
- Acknowledgements
- I Basic topics
- II Advanced topics
- 9 Advanced embedding methods
- 10 Chaotic data and noise
- 11 More about invariant quantities
- 12 Modelling and forecasting
- 13 Non-stationary signals
- 14 Coupling and synchronisation of nonlinear systems
- 15 Chaos control
- A Using the TISEAN programs
- B Description of the experimental data sets
- References
- Index
15 - Chaos control
Published online by Cambridge University Press: 06 July 2010
- Frontmatter
- Contents
- Preface to the first edition
- Preface to the second edition
- Acknowledgements
- I Basic topics
- II Advanced topics
- 9 Advanced embedding methods
- 10 Chaotic data and noise
- 11 More about invariant quantities
- 12 Modelling and forecasting
- 13 Non-stationary signals
- 14 Coupling and synchronisation of nonlinear systems
- 15 Chaos control
- A Using the TISEAN programs
- B Description of the experimental data sets
- References
- Index
Summary
Regarding applications, chaos control is surely one of the most exciting outcomes of the theory of dynamical systems. [See Ott & Spano (1995) for a nontechnical account.] There exists an impressive list of experiments where chaos control has been applied successfully. Examples include laser systems, chemical and mechanical systems, a magneto-elastic ribbon, and several others. Additionally, there are claims that the same mechanism also works in the control of biological systems such as the heart or the brain. After the pioneering work of Ott, Grebogi & Yorke (1990), often referred to as the “OGY method”, a number of modifications have been proposed. We want to focus here on the original method and only briefly review some modifications which can simplify experimental realisations. We give only a few remarks here on the time series aspects of chaos control technology. For further practical hints, including experimental details, the reader is asked to consult the rich original literature. (See “Further reading” below.)
In most technical environments chaos is an undesired state of the system which one would like to suppress. Think, for instance, of a laser which performs satisfactorily at some constant output power. To increase the power the pumping rate is raised. Suddenly, due to some unexpected bifurcation, the increased output starts to fluctuate in a chaotic fashion. Even if the average of the chaotic output is larger than the highest stable steady output, such a chaotic output is probably not desired. Chaos control can help to re-establish at least a regularly oscillating output at a higher rate, with judiciously applied minimal perturbations.
- Type
- Chapter
- Information
- Nonlinear Time Series Analysis , pp. 304 - 320Publisher: Cambridge University PressPrint publication year: 2003