A Novel Controller for Bipedal Locomotion Integrating Feed-Forward and Feedback Mechanisms

Abstract

It has been recognized that bipedal locomotion is controlled using feed-forward (e.g., patterned) and feedback (e.g., reflex) control schemes. However, most current controllers fail to integrate the two schemes to simplify speed control of bipedal locomotion. To solve this problem, we here propose a patterned muscle-reflex controller integrating feed-forward control with a muscle-reflex controller. In feed-forward control, the pattern generator is modeled as a Matsuoka neural oscillator that produces four basic activation patterns that mimic those extracted experimentally via electromyograms (EMGs). The associated weights of the patterns for 16 Hill-type musculotendon units (MTUs) are calculated based on a predictive model of muscle excitations under human locomotion. The weighted sums of the basic activation patterns serve as the pre-stimulations to muscle-reflex control of the Hill-type MTUs actuating a 2D-simulated biped. As a result, the proposed controller enables the biped to easily regulate its speed on an even ground by only adjusting the descending input. The speed regulation does not require re-optimizations of the controller for various walking speeds, compared to pure muscle-reflex controllers.