On-line neural network predictive control for swing phase control of MR damper prosthetic knee

The prosthetic knees have been improved and developed to support the amputee to be able to walk as normal people and help them on a daily basis. This research is concerned with a swing phase of a semi-active prosthetic knees utilizing magnetorheological (MR) damper. Although the referred work which use a neural network predictive control (NNPC) has a satisfying results with low error, it has a possibility to improve the error at certain walking speeds. Therefore, an alternative algorithm for a semi-active prosthetic knee is proposed. The modified version of NNPC using online training scheme with scaled conjugate gradient training algorithm is applied to substitute the initial version of NNPC for the prosthetic knee model to estimate appropriate knee angle. The results from simulation using proposed controller show the improvement in error over the initial version of NNPC at absolutely slow and absolutely fast walking speeds while the results using other walking speeds are comparable.