Markovian Transparency Control of an Exoskeleton Robot

Abstract

In wearable robotics, certain applications require the robot to be transparent, i.e., imperceptible to the user. This is a very difficult cooperative control task due to the inherent coupling between human and robot, unpredictable human movements, and user-dependent behavior. In this letter, we propose a novel transparency controller based on discrete-time Markovian jump linear systems to minimize the human-robot interaction forces of an exoskeleton robot during walking. Our model-based stochastic control approach describes a gait cycle as an event-dependent Markov chain and uses a given transition matrix to switch between them. An IMU-based Kalman filter is used to perform real-time human state estimation and gait phase detection. The robustness and effectiveness of the proposed controller are demonstrated with experiments on a lower-limb exoskeleton driven by series elastic actuators.