Advanced biped gait generator using NARX-MLP neural model optimized by enhanced evolutionary algorithm
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https://doi.org/10.15625/0866-7136/17230Keywords:
small-sized biped, walking pattern generator (WPG), Nonlinear Auto-Regressive eXogenous (NARX) model, model, Enhanced Differential Evolution (EDE) optimization technique, zero-moment-point (ZMP) conceptAbstract
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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