A Learning Quasi-stiffness Control Framework of a Powered Trans-femoral Prosthesis for Adaptive Speed and Incline Walking

Impedance-based control represents a prevalent strategy in the powered transfemoral prostheses because of its ability to reproduce natural walking. However, most existing studies have developed impedance-based prosthesis controllers for specific tasks, while creating a task-adaptive controller for variable-task walking continues to be a significant challenge. This article proposes a task-adaptive quasi-stiffness control framework for powered prostheses that generalizes across various walking tasks, enhancing the gait symmetry between the prosthesis and intact leg. A Gaussian Process Regression (GPR) model is introduced to predict the target features of the human joint’s angle and torque in a new task. Subsequently, a Kernelized Movement Primitives (KMP) is employed to reconstruct the torque-angle relationship of the new task from multiple human reference trajectories and estimated target features. Based on the torque-angle relationship of the new task, a quasi-stiffness control approach is designed for a powered prosthesis. Finally, the proposed framework is validated through practical examples, including varying speeds and inclines walking tasks. Notably, the proposed framework not only aligns with but frequently surpasses the performance of a benchmark finite state machine impedance controller (FSMIC) without necessitating manual impedance tuning and has the potential to expand to variable walking tasks in daily life for the transfemoral amputees.