Gait Control Based on a Disturbance Observer to Improve the Gait Synchronization of a Lower Extremity Exoskeleton Robot

Abstract

This research develops the gait control of a lower-extremity exoskeleton robot that augments the muscular power of a human while carrying a payload. The exoskeleton robot requires many sensors to acquire the motion intentions of the human. This research applies the minimum number of sensors in order to estimate the human’s motion intention without using force or torque sensors. It also researches the gait control method in order to improve the gait synchronization between the human and the lower-extremity exoskeleton robot. In order to research gait control algorithm, a lower-extremity exoskeleton robot that is suitable for the industrial or military sectors is developed. The developed lower-extremity exoskeleton robot can carry the maximum payload of 30kg with a maximum gait speed of 6km/h on level terrain, stairs, and a slope. The gait of a human was analyzed to design the lower-extremity exoskeleton robot. In order to derive the design parameters, the gait data were obtained through an experiment. The developed lower-extremity exoskeleton robot has 7DOF on one leg, including 3DOF on the hip joint, 1DOF on the knee joint, and 3DOF on the ankle joint. In the sagittal plane, the hip joints and knee joints were designed with the electric motors and harmonic drives, and the ankle joints was designed with a 1 DOF quasi-passive joints. The hip and ankle joints on the coronal and horizontal planes both adapted the passive joint in order to resemble human movement. The necessary sensors for the algorithm were also selected and an insole sensor was developed to detect the gait phase. In order to develop an algorithm to acquire the motion intention of the exoskeleton robot, the existing algorithm for acquiring motion intention were analyzed and defects of the intention acquisition algorithm using the force sensor were derived. This research applied the disturbance observer to acquire the human’s motion intention without using a force sensor. The intention-acquiring algorithm, which is based on the disturbance observer, has been explained in terms of the 1DOF and multi-DOF systems. The LuGre model was applied to improve the performance of the intention-acquiring algorithm so that the friction parameters of the harmonic gear could be estimated and compensated. The proposed algorithm was verified through an experiment that utilized the developed of the one-DOF simulation and 1DOF system. To control the gait of the lower-extremity exoskeleton robot, the models were defined based on the gait phase, and the rigid body model was defined. The required angular velocity and angular acceleration for the algorithm were estimated based on the angle, and the gait-phase-detection algorithm was developed in order to detect the gait phase based on the COP. The intention-acquiring algorithm was designed based on the disturbance observer and applied to the model of the lower-extremity exoskeleton robot. Finally, the gait-control algorithm of the lower-extremity exoskeleton robot was developed by integrating the algorithms and designing the controller. The proposed gait-control algorithm was applied to the developed lower-extremity exoskeleton robot, and an experiment was conducted to verify the system. Existing verification methods for the exoskeleton robot were analyzed and the biological signal was measured for the proposed verification method. The experiment was conducted separately through test cases with and without the exoskeleton robot. When walking on level terrain, the oxygen consumption (VO2) and carbon dioxide (CO2) emissions were measured using a metabolic measurement system and the metabolic power was calculated. In addition, the vGRF measurement system measured the human’s vGRF value. The human’s vGRF value was measured by the vGRF measurement system when walking on a slope. According to the results, the metabolic power decreased by approximately 33.51%, and the vGRF decreased by approximately 44.28%. As a result, the lower-extremity exoskeleton robot was proven to augment muscular power.