A Bio-Signal Enhanced Adaptive Impedance Controller for Lower Limb Exoskeleton

The problem of human-exoskeleton interaction with uncertain dynamical parameters remains an open-ended research area. It requires an elaborate control strategy design of the exoskeleton to accommodate complex and unpredictable human body movements. In this paper, we proposed a novel control approach for the lower limb exoskeleton to realize its task of assisting the human operator walking. The main challenge of this study was to determine the human lower extremity dynamics, such as the joint torque. For this purpose, we developed a neural network-based torque estimation method. It can predict the joint torques of humans with surface electromyogram signals (sEMG). Then an radial basis function neural network (RBF NN) enhanced adaptive impedance controller is employed to ensure exoskeleton track desired motion trajectory of a human operator. Algorithm performance is evaluated with two healthy subjects and the rehabilitation lower-limb exoskeleton developed by Shenzhen Institutes of Advanced Technology (SIAT).