Research On Finite Time Control Method Of Rehabilitation Robot Based On Estimation Of Man-machine Uncertainty

With the increasingly serious problem of population aging,the number of patients with moderate diseases of stroke is increasing,and the number of patients with walking disorder is also increasing.At the same time,China is in short of medical resources at this stage,and the rehabilitation training resources for patients with walking disorder are insufficient.In order to alleviate the shortage of medical resources and reduce the economic burden of families and society,the research on rehabilitation walking training robot has attracted the attention of researchers.At present,many research results on the tracking control of rehabilitation walking training robot do not consider the motion performance of the rehabilitation robot in the transient time.If the initial error of the rehabilitation robot is large,and the transient tracking time of the rehabilitation robot is too long,the rehabilitation robot may have collision and other dangers.In order to solve this problem,this paper studies the finite time control problem of omnidirectional rehabilitation walking training robot.The main contents are as follows:According to the system structure of rehabilitation training robot,the kinematics model and dynamics model are analyzed,and the kinematics model and dynamics model considering the gravity shift are established.Stochastic Configuration Network(SCN)is used to estimate the man-machine uncertainties in the dynamics model,and a dynamics model with manmachine uncertainties is established.Based on the dynamics model with man-machine uncertainty,a finite time compensation controller is designed and its finite time stability is analyzed.Simulation results show that SCN is effective to estimate the man-machine uncertainty of rehabilitation training robot.Further simulation experiments show that when the rehabilitation training robot has a large initial error,the controller can track the trajectory specified by the doctor in a limited time,and has good robustness to the man-machine uncertainty of the rehabilitation training robot.The parameters of the finite time compensation controller of the rehabilitation training robot were optimized based on the gray wolf algorithm,and the fluctuation and overshoot of the trajectory error were minimized and the adjustment time was minimized.The simulation results show that the controller parameters selected by the gray wolf optimization algorithm can shorten the tracking transient time of the rehabilitation robot and improve the tracking performance of the man-machine system.