Geometric optimization of magnetorheological damper for prosthetic ankles using artificial neural networks

In this work, the primary design constraints of a magnetorheological (MR) actuator and its stroke dimension have been found based on biomechanical requirements and anthropometric constraints of the ankle in a transtibial prosthesis. Based on the inverted slider-crank mechanism models, the force controller parameters of the MR damper are identified. Parameters of the MR dampers are evaluated through optimization that minimises the error between the prosthetic ankle moment and the desired ankle moment for normal level ground walking from experimental data. Furthermore, an artificial neural network (ANN) framework for the MR valve is developed where a three-layered ANN model has been utilised to forecast the magnetic flux density (MFD) across different regions of the MR valve. The data have been generated from an ANSYS-APDL software package using finite element magnetostatic analysis (FEMS). The ANN model outcomes match the FEMS results reasonably well. Finally, the ANN model is employed to find MFD and is used to optimize the MR valve. Optimal solutions are obtained that satisfy the goal function of maximising the damper force and minimising the energy consumption and weight of the MR damper. Subsequently, the optimized MR damper has been fabricated and tested experimentally and it has been found to produce enough force to act as an actuator in a prosthetic ankle.