Study on inverse dynamics of full-body powered pseudo-anthropomorphic exoskeleton using neural networks

Abstract

This paper deals with a methodology to create a mathematical model in order to analyze a novel design of a full-body powered pseudo-anthropomorphic exoskeleton (32 DoF). The expressions for torque used to generate a training data-set of kinematic and kinetic parameters of the system, are calculated using Lagrangian and Denavit-Hartenberg joint parameters; inclusive of reaction force on the lower limbs by the upper limbs of the exoskeleton. This training data-set is used to train a multilayer feed-forward neural network for generation of the instantaneous torque values for joint actuation; the network is trained using Levenberg- Marquardt algorithm (LMA) to solve the mean squared deviation curve fitting. This method can serve as a replacement for the inverse dynamics model deployed to solve torque calculation problems within a fraction of second; and is tested by comparison of the output torque of lower torso with that of sample gait cycle data.