Swing Phase Control of Semi-Active Prosthetic Knee Using Neural Network Predictive Control With Particle Swarm Optimization.

In recent years, intelligent prosthetic knees have been developed that enable amputees to walk as normally as possible when compared to healthy subjects. Although semi-active prosthetic knees utilizing magnetorheological (MR) dampers offer several advantages, they lack the ability to generate active force that is required during some states of a normal gait cycle. This prevents semi-active knees from achieving the same level of performance as active devices. In this work, a new control algorithm for a semi-active prosthetic knee during the swing phase is proposed to reduce this gap. The controller uses neural network predictive control and particle swarm optimization to calculate suitable command signals. Simulation results using a double pendulum model show that the generated knee trajectory of the proposed controller is more similar to the normal gait than previous open-loop controllers at various ambulation speeds. Moreover, the investigation shows that the algorithm can be calculated in real time by an embedded system, allowing for easy implementation on real prosthetic knees.