Minimizing Human-Exoskeleton Interaction Force Using Compensation for Dynamic Uncertainty Error with Adaptive RBF Network

Abstract

A critical issue in the control of exoskeleton systems is unknown nonlinear dynamic properties of the system. The improper estimation of those unknown properties can cause considerable human-exoskeleton interaction force during human's movements. It is really challenging to exactly estimate the parameters of dynamic models. In this paper, we propose a novel exoskeleton control algorithm to both compensate for the dynamic uncertainty error and minimize the human-exoskeleton interaction force. We have built a virtual torque controller based on dynamic models of a lower exoskeleton and have used an approximation of a Radial Basis Function (RBF) neural network to compensate for the dynamic uncertainty error. By doing so, we avoid using complicated force sensors installed on the human-exoskeleton interface and minimize the physical Human-Robot Interaction (pHRI) force. Moreover, we introduce the prototype of our exoskeleton system, called `PRMI' exoskeleton system. Finally, we validated the proposed algorithm on this system, and the experimental results show that the proposed control algorithm provides a good control quality for the `PRMI' exoskeleton system by compensating for dynamic uncertainty error.