Variable walking speed controller of MR damper prosthetic knee using neural network predictive control

Semi-active prosthetic knees utilizing magnetorheological (MR) damper have been introduced to help transfemoral amputees to improve their quality of life. However, they lack the ability to generate power required to achieve the normal gait of healthy people. The aim of this paper is to propose a novel control algorithm for MR damper prosthetic knee, focusing on swing phase of the gait cycle. The proposed controller is developed by using a neural network predictive control optimized using constrained nonlinear optimization. It provides the ability to predict future knee angle trajectory when a certain command voltage is applied in order to determine the optimal command voltage. Moreover, the proposed algorithm is designed to support amputees at walking speeds. Performance of the proposed controller has been evaluated in an offline simulation compared to the normal gait of healthy people. The results show that the proposed controller can generate the knee trajectory with minimal errors.