Energy-efficient limit cycle walking in disturbance based on nonlinear model predictive control

Abstract

Abstract In this study, we present a novel approach to generate limit cycle walking using nonlinear model predictive control (NMPC). Output-zeroing control is now widely used as a control method to limit cycle walking. This control offers strong feedback to the desired trajectory and the generation of energy-efficient and robust limit cycle walking. However, we observed that this method disables the natural dynamics of the robot, leading to problems regarding energy efficiency during walking. This study demonstrates that the energy consumption of walking using the output-zeroing control increases significantly in a disturbed environment. To overcome this limitation, the proposed approach leverages the robot’s dynamics using NMPC to achieve energy-efficient walking even in a disturbed environment. We demonstrate the practicality of the proposed method using two different simulation environments.