Research On Control Algorithm And System Design Of Lower Limb Rehabilitation Robot

After entering the 21st century,China has made tremendous leaps in various fields,and the population aging trend has become more and more obvious,especially in recent years,it has gradually entered an aging society.With the rapid increase in the elderly population,various elderly diseases,especially neurological diseases and cerebrovascular diseases,will follow,which will cause limb motor dysfunction.At present,the main treatment method for these conditions is professional rehabilitation training by rehabilitation doctors.However,the rehabilitation doctor’s treatment is relatively simple and has great defects.Therefore,the lower extremity exoskeleton rehabilitation robot technology has developed rapidly in recent years.This technology can help patients to undergo rehabilitation training.It is a combination of robot technology and traditional rehabilitation medicine.For patients with hemiplegia,the condition is divided into multiple different stages,and different rehabilitation training methods are required at different stages.Therefore,rehabilitation training is also divided into two ways to correspond to different stages,which are passive rehabilitation training and Active rehabilitation training.In the early stage of rehabilitation,the patient was basically unable to move autonomously.At this time,the robot led the patient to train.In the middle and late stages of rehabilitation,the patient’s limbs are already capable of movement,and the patient’s lower extremity exoskeleton robot assists in the rehabilitation training.Authoritative research shows that when patients take the initiative to perform rehabilitation training,the injured nervous system can recover more quickly.This article proposes a variety of rehabilitation strategies to cope with the different rehabilitation stages of patients.This article first builds a lower limb exoskeleton rehabilitation robot system,builds a real-time system framework based on the QNX system,and sets up a ground experiment platform and a treadmill experiment platform,respectively.Later,DH parameter method and Lagrangian method were used to establish exoskeleton kinematics and dynamics model,and then the correctness of the model was verified by simulation.The transformation relationship between the posture of the robot end joints and the angles of each joint was obtained.Laid a solid theoretical foundation.VICON optical motion capture system was used to collect normal human gait,fit and filter it,and finally obtain the gait reference trajectory.After obtaining the gait trajectory,for the early stage of rehabilitation,passive gait trajectory tracking passive control was used for passive training.In the middle and late stages of rehabilitation,based on traditional admittance control,a new active rehabilitation training algorithm based on fuzzy adaptive admittance control is proposed for training.When the patient is undergoing rehabilitation training,the controller will adjust the admittance parameters online according to the human-computer interaction force information to improve the patient’s active rehabilitation training effect.Finally,experiments were performed on lower extremity exoskeleton rehabilitation robots using normal adults.The various control algorithms mentioned in the thesis were verified separately,and the results were compared to verify the feasibility of the algorithm used in this thesis.An experiment on the effectiveness of exoskeleton gait training was also carried out.The exoskeleton constructed in this thesis can basically meet the expected results.