Advanced biped gait generator using NARX-MLP neural model optimized by enhanced evolutionary algorithm

Author affiliations

Authors

  • Tran Thien Huan Faculty of Applied Sciences (FAS), HCM City University of Technology and Education (HCMUTE), Ho Chi Minh City, Vietnam https://orcid.org/0000-0001-9939-6997
  • Ho Pham Huy Anh \(^1\)Faculty of Electrical-Electronics Engineering (FEEE), Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam
    \(^2\)Vietnam National University Ho Chi Minh City (VNU-HCM), Linh Trung Ward, Thu Duc City, Ho Chi Minh City, Vietnam     https://orcid.org/0000-0001-7353-8205

DOI:

https://doi.org/10.15625/0866-7136/17230

Keywords:

small-sized biped, walking pattern generator (WPG), Nonlinear Auto-Regressive eXogenous (NARX) model, model, Enhanced Differential Evolution (EDE) optimization technique, zero-moment-point (ZMP) concept

Abstract

A novel biped walking pattern combining robust zero-moment-point ZMP technique and pre-determined foot-lifting value is proposed in this paper. The implementation of suggested approach contains following stages. Initially, a one-step ZMP curve for a small-sized humanoid is created using the 3rd-order interpolating equation, with pre-determined velocity responding the ZMP concept. The next step, biped gait planning is modeled as a non-linear MIMO plant including ten degree-of-freedom DOF. Then, the installation of a biped walking pattern generator (WPG) based on the new hybrid Neural-NARX model is completed. Eventually, the novel Enhanced Differential Evolution (EDE) technique is applied to optimally identify the weights of the hybrid Neural-NARX structure, for ensuring robust robot walking in terms of desired ZMP trajectories and pre-determined foot-lifting value. All case studies confirm that it is surely provide a biped WPG satisfying both of the effectiveness and high robustness. The verification of the newly proposed WPG is adequately tested via both simulation and experiment results.

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Published

30-09-2022

How to Cite

[1]
T. T. Huan and H. P. H. Anh, Advanced biped gait generator using NARX-MLP neural model optimized by enhanced evolutionary algorithm, Vietnam J. Mech. 44 (2022) 249–265. DOI: https://doi.org/10.15625/0866-7136/17230.

Issue

Vol. 44 No. 3 (2022)

Section

Research Article

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This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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