Numerical and analytical methods for synthesis of central pattern generators

Authors:
Yonghui Hu;Wei Zhao;Jianhong Liang;Tianmiao Wang
Affiliations:
School of Control and Computer Engineering, North China Electric Power University, Beijing, P. R. China, School of Mechanical Engineering and Automation, Beihang University, Beijing, P.R. China;IBM Research-China, Beijing, P.R. China;School of Mechanical Engineering and Automation, Beihang University, Beijing, P.R. China;School of Mechanical Engineering and Automation, Beihang University, Beijing, P.R. China
Venue:
ICIRA'12 Proceedings of the 5th international conference on Intelligent Robotics and Applications - Volume Part III
Year:
2012

Citing 5
Cited 0

Weakly connected neural networks

Weakly connected neural networks
Engineering entrainment and adaptation in limit cycle systems: From biological inspiration to applications in robotics

Biological Cybernetics - Special Issue: Dynamic Principles
Stable concurrent synchronization in dynamic system networks

Neural Networks
2008 Special Issue: Central pattern generators for locomotion control in animals and robots: A review

Neural Networks
Multivariable harmonic balance for central pattern generators

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents numerical and analytical methods for synthesis of a CPG network to acquire desired locomotor patterns. The CPG network is modeled as a chain of coupled Hopf oscillators with a coupling scheme that eliminates the influence of afferent signals on amplitude of the oscillator. The numerical method converts the related CPG parameters into dynamical systems that evolve as part of the CPG network dynamics. The frequency, amplitude and phase relations of teaching signals can be encoded by the CPG network with the proposed learning rules. For direct specification of the phase relations, the expression that defines the dependence of phase difference on coupling weights is analytically derived. The ability of the numerical methods to learn instructed locomotor pattern is proved with simulations. The effectiveness of the analytical method is also validated by the numerical results.