Powered prostheses hold promises to restore adaptive and robust locomotor functions to lower-limb amputees. However, their daily use is still challenged by several shortcomings, on top of which those related to their control methods. This paper reports the development of a compliant controller for a transfemoral prosthesis that combines an online predictive torque component with a feedback error correction mechanism. The controller architecture relies on joint position tracking. The predictive module is based on the Locally Weighted Projection Regression (LWPR) algorithm that achieves nonlinear function approximation of a dynamic model of the prosthesis joints. The performance of the proposed control strategy are assessed with a simulated biped walker with a unilateral transfemoral amputation. Results show that the LWPR-based module provides accurate predictions of the ankle and knee torques, resulting in a precise position tracking. This allows reducing the gains of the feedback error correction mechanism by one order of magnitude, leading to a feedback contribution to the total joint torque lower than 3\% and 8\% for the ankle and the knee joints respectively. The prosthesis compliance for both joints is enhanced accordingly. In addition, the control architecture is robust to speed changes while the joint dynamic internal model is continuously learned. This approach is thus promising for the development of adaptive controllers for lower-limb prostheses.
Online Learning of the Dynamical Internal Model of Transfemoral Prosthesis for Enhancing Compliance
2021-01-01
IEEE Robotics and Automation Letters, Vol. 6, no. 4, p. 6156-6163 (2021)
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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