Identification of a Step-And-Brake Controller of a Human Based on Prediction of Capturability - Kojima, Miharu; Sugihara, Tomomichi (Frontiers in Robotics and AI, 2022)
An explicit mathematical form of a human’s step-and-brake controller is identified through motion measurement of the human subject. The controller was originally designed for biped robots based on the reduced-order dynamics and the model predictive control scheme with the terminal capturability condition, and is compatible with both stand-still and stepping motions. The minimal number of parameters facilitates the identification from measured trajectories of the center of mass and the zero-moment point of the human subject. In spite of the minimality, the result only suited the human’s behaviors well with slight modifications of the model by taking direction- dependency of the natural falling speed and the inertial torque about the center of mass into account. Further more, the parameters are successfully identified even from the first half of motion sequence, which means that the proposed method is available in designing on-the-fly systems to evaluate balancing abilities of humans and to assist balances of humans in walking.