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Related Experiment Videos

Chaos control by using Motor Maps.

Paolo Arena1, Luigi Fortuna, Mattia Frasca

  • 1Dipartimento Elettrico, Elettronico e Sistemistico Universita degli Studi di Catania, Viale A. Doria 6, 95125, Catania, Italy.

Chaos (Woodbury, N.Y.)
|June 5, 2003
PubMed
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This study introduces a novel chaos control method using an unsupervised neural network called a Motor Map. This adaptive controller effectively manages chaotic systems and demonstrates high performance, even when reference trajectories change.

Area of Science:

  • Chaos theory
  • Artificial intelligence
  • Control systems engineering

Background:

  • Chaos control is crucial for managing complex dynamical systems.
  • Existing methods often require precise system models or extensive training.
  • Unsupervised learning offers a potential avenue for adaptive chaos control.

Purpose of the Study:

  • To propose a new chaos control method utilizing an unsupervised neural network (Motor Map).
  • To investigate the feasibility of hardware implementation for the proposed control system.
  • To evaluate the performance of the Motor Map in controlling chaotic attractors with different reference trajectories.

Main Methods:

  • A feedback entrainment scheme was adopted, using a reference chaotic system to guide a controlled system.

Related Experiment Videos

  • The Motor Map provided adaptive time-varying gain for the feedback signal.
  • Simulations were conducted on a Double Scroll Chua Attractor and other chaotic systems, with a focus on hardware realization constraints (e.g., limited neurons).
  • Control involved feeding back only two state variables.

    • Main Results:

    • The Motor Map achieved good performance in terms of settling time and steady-state errors with a few neurons.
    • The adaptive controller demonstrated high performance, particularly when reference trajectories were switched.
    • Neuron specialization was observed, with distinct groups learning control laws for different reference trajectories.
    • Experimental results from a discrete electronic realization confirmed simulation findings.

    Conclusions:

    • The proposed Motor Map offers an effective and adaptive solution for chaos control.
    • The method is suitable for hardware implementation and shows robustness to changes in reference trajectories.
    • Neuron specialization enhances the controller's adaptability and efficiency.