Evolutionary Optimization of Artificial Neural Networks for Prosthetic Knee Control

Abstract

This chapter discusses closed-loop control development and simulation results for a semi-active above-knee prosthesis. This closed-loop control is a delta control that is added to previously developed open-loop control. The control signal consists of two hydraulic valve settings. These valves control a rotary actuator that provides torque to the prosthetic knee. Closed-loop control using artificial neural networks (ANNs) are developed, which is an intelligent control method. The ANNs are trained with biogeography-based optimization (BBO), which is a recently developed evolutionary algorithm. This research contributes to the field of evolutionary algorithms by demonstrating that BBO is successful at finding optimal solutions to real-world, nonlinear, time varying control problems. The research contributes to the field of prosthetics by showing that it is possible to find effective closed-loop control signals for a newly proposed semi-active hydraulic knee prosthesis. The research also contributes to the field of ANNs; it shows that they are able to mitigate some of the effects of noise and disturbances that will be common in normal operation of a prosthesis and that they can provide better robustness and safer operation with less risk of stumbles and falls. It is demonstrated that ANNs are able to improve average performance over open-loop control by up to 8% and that they show the greatest improvement in performance when there is high risk of stumbles.