Research On Control Strategy Of Soft Lower Limb Exoskeleton

As a new type of assisting exoskeleton,the soft lower limb exoskeleton has a great prospect in the fields of medical rehabilitation,assisting the elderly and the disabled,and sports enhancement.The soft exoskeleton as a human-machine interaction system involves the recognition of human intention,assistance strategy selection and robot control.The performance of soft lower limb exoskeleton is restricted by the highly coupled humanmachine interaction.This thesis focuses on these problems,which will promote the further development of soft exoskeleton.Walking on different terrains leads to different biomechanics,which motivates the development of exoskeletons for assisting on walking according to the type of terrain.To determine the assisting strategy in different terrains,the motion capture system is used to obtain the joint torque of human lower limb in different slope terrains.Based on the biological torque of human walking at three different slopes,a novel strategy is developed to improve the performance of assistance.The human intension identification includes the prediction of the gait cycle and the recognition of walking terrain.A gait cycle predictor is designed based on one-step Winer prediction in this paper.The terrain identification which based on the feature of intersection angle can achieve online recognition.According to the hip angle and angular velocity,the support vector machines model is used to design a classifier,which can achieve 100% correct classification under three types of terrain.This method solves the limitations of the intersection angle feature method.The kinematic and dynamic model of the system is analyzed in this paper,and the approximate dynamic model of the system is obtained through the method of system identification.To reduce the tracking error in the walking,the iterative learning control of parameter optimization is improved to make it suitable for the soft exoskeleton,and a controller which automatic switching according to the walking terrain is designed based on this method.The controller can automatically adjust the assistance strategy according to the terrain and gait are stable or not,it has a satisfactory performance in different terrains.The soft exoskeleton used in this paper can assist in hip joint extension and flexion and knee joint extension.The force tracking experiment was conducted on three experimenters.After adding the parameter optimization iterative learning control and after20 iterations,the robot has a significant reduction in the tracking error of the expected assist strategy.The root-mean-square error dropped by 33.00%,46.45% and 51.22% in the terrain of downhill,flat ground and uphill terrain.To obtain the metabolic rate,three subjects walked on a treadmill,for 10 min on each terrain,at a speed of 4 km/h under both conditions of wearing and not wearing the soft exoskeleton.Results showed that the metabolic rate was decreased with the increasing slope of the terrain.The reductions in the net metabolic rate in the experiments on the downhill,flat ground,and uphill were,respectively,9.86%,12.48% and 22.08% compared to the condition of not wearing the soft exoskeleton,where their corresponding absolute values were 0.28 W/kg,0.72 W/kg,and 1.60 W/kg.