Intelligent control of a prosthetic ankle joint using gait recognition. (April 2016) - Mai, Anh; Commuri, Sesh
Abstract: Desire for better prosthetic feet for below-knee amputees has motivated the development of several active and highly functional devices. These devices are equipped with controlled actuators in order to replicate biomechanical characteristics of the human ankle, improve the amputee gait, and reduce the amount of metabolic energy consumed during locomotion. However, the functioning of such devices on human subjects is difficult to test due to changing gait, unknown ankle dynamics, complicated interaction between the foot and the ground, as well as between the residual limb and the prosthesis. Commonly used approaches in control of prosthetic feet treat these effects as disturbances and ignore them, thereby degrading the performance and efficiency of the devices. In this paper, an artificial neural network-based hierarchical controller is proposed that first recognizes the amputees' intent from the actual measured gait data, then selects a displacement profile for the prosthetic joint based on the amputees' intent, and then adaptively compensates for the unmodeled dynamics and disturbances for closed loop stability with guaranteed tracking performance. Detailed theoretical analysis is carried out to establish the stability and robustness of the proposed approach. The performance of the controller presented in this paper is demonstrated using actual gait data collected from human subjects. Numerical simulations are used to demonstrate the advantages of the proposed strategy over conventional approaches to the control of the prosthetic ankle, especially when the presence of noise, uncertainty in terrain interaction, disturbance torques, variations in gait parameters, and changes in gait are considered. Highlights: Model-free hierarchical control structure adapts in real time to changes in user gait. Spatial-kinetic measurements during gait are used to recognize the gait of the user. Commanded ankle displacement profiles are generated for the user gait in real time. Stability and robustness of the approach are rigorously proven using Lyapunov theory. Tracking performance is showed using actual gait data collected from clinical studies.