Tracking control of a planar five-link bipedal walking system with point contact, considering self-impact joint constraint by adaptive neural network method

Abstract

In order to achieve the practical characteristics of natural bipedal walking, a key feature is to realize "the straight knee state of walking" during stance and swing motions. Considering a straight knee necessitates that the shank link of each leg not to undergo the rotation angles which are greater than that of the thigh link. For this purpose, various methods have been proposed; the joint selfimpact constraint has been suggested for energy-efficient (natural) bipedal walking while realizing the straight knee constraint. The prominent objective of this research is to present a model based control method for trajectory tracking of a normal humanlike bipedal walking, by considering the joint self-impact constraint. To achieve this objective, the dynamical equations of motion of an unconstrained biped are taken, developed and then modified to consider the joint self-impact constraint at the knee joint. To control this complicated dynamical system, the available anthropometric normal gait cycle data are taken to generate the desired trajectories of the thigh and knee joints of the self-impact biped. Due to the existence of complex nonlinear terms in the dynamical governing equations of self-impact biped, the authors propose to design a nonlinear intelligent controller by taking advantage of the adaptive neural network control method, which neither requires the evaluation of inverse dynamical model nor the time consuming training process. According to the simulation results, the tracking control of the biped robot is accomplished well and the biped walking seems naturally, despite of involving complex nonlinear terms in the dynamical governing equations of the self-impact biped.