Optimizing Neural Oscillators for Rhythmic Movement Control

Abstract

A parameter tuning scheme for the neural oscillator is addressed to achieve biologically inspired robot control architectures based on a neural oscillator. It would be desirable to determine appropriately unknown parameters of the neural oscillator to accomplish a task of rhythmic movement under various changes of environment. Human or animal exhibits natural dynamics with efficient and performs robust motions against unexpected disturbances or environment changes. The neural oscillator needs to be tuned using its optimal parameters to generate such natural movement. As simple examples, this paper connects the neural oscillator to a pendulum system and a rotating crank system. To determine the optimal parameters of the neural oscillator for the examples, the optimization scheme based on the Simulated Annealing (SA) method is used. We verify the performance of the given tasks with the obtained optimal parameters of the neural oscillator, showing the adaptation motions of the example systems with entrainment property in numerical simulations.