Nonlinear predictive control applied to a biped walker with adjustable step length using a passive walking-based reference generator

This paper presents the use of passive dynamics to generate the sagittal plane references for a biped walker. In order to achieve an adjustable gait width, a neural network reference generator, trained with passive walking data on different slopes, was applied. The control algorithm was implemented with an Nonlinear Model Predictive Control (NMPC) strategy, in order to perform multiple-input multiple-output reference tracking while maintaining balance. The main contributions of the present work are using a kneed passive walker model for reference generation, which creates gait profiles that are more natural than the compass gait model, presenting a low computational cost, since only the forward kinematic model is used, and enabling adjustable gait width, due to the use of a neural network trained with data from the passive walker. Simulation results using V-REP (Virtual Robot Experimentation Platform) are presented, using a sagittal kneed walker with a torso and arms, demonstrating the effectiveness of the proposed method.