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Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
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Phase reduction theory for hybrid nonlinear oscillators.

Sho Shirasaka1, Wataru Kurebayashi2, Hiroya Nakao3

  • 1Graduate School of Information Science and Engineering, Tokyo Institute of Technology, O-okayama 2-12-1, Meguro, Tokyo 152-8552, Japan.

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We developed a new phase reduction theory for hybrid dynamical systems, enabling analysis and control of nonlinear oscillators. This advances understanding of synchronization in systems like bipedal locomotion.

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Area of Science:

  • Nonlinear Dynamics
  • Control Theory
  • Robotics

Background:

  • Hybrid dynamical systems model rhythmic phenomena via switching dynamics.
  • Phase reduction theory traditionally applies to smooth systems, limiting analysis of hybrid oscillators.
  • Many real-world rhythmic systems are hybrid, lacking smooth models.

Purpose of the Study:

  • To develop a generalized phase reduction theory for hybrid dynamical systems.
  • To enable analysis, control, and optimization of nonlinear oscillators with intractable smooth models.
  • To investigate synchronization properties of hybrid oscillators.

Main Methods:

  • Generalized phase reduction theory for weakly perturbed hybrid limit cycles.
  • Analysis of injection locking using periodic forcing.
  • Application to a physical model of bipedal locomotion.

Main Results:

  • Demonstrated a novel phase reduction theory for hybrid systems.
  • Revealed ultrafast and robust entrainment in hybrid oscillators.
  • Observed logarithmic scaling at the synchronization transition.

Conclusions:

  • The generalized theory facilitates synchronization analysis and control of hybrid oscillators.
  • The findings offer insights into synchronization dynamics in complex rhythmic systems.
  • Applicable to diverse fields including robotics and neuroscience.