Research On Active Training Control Method Of Lower Limb Rehabilitation Robot

The frequent occurrence of heart and brain diseases and accidents lead to the increase of lower limb movement disorders year by year.The use of rehabilitation robot for rehabilitation training is the main development direction at present.Among them,active training is suitable for patients with autonomous motor ability,but the lag and accuracy of tracking in active training need to be improved.Therefore,this paper proposes a predictive feedback force position hybrid control model to improve the rapidity and accuracy of system tracking.The prediction model includes plantar pressure prediction and lower limb motion trajectory prediction.This topic selection of plantar pressure signals as a signal to identify trend of lower limb movement applied to collect the trainee of plantar pressure signal to predict the next step of plantar pressure,to obtain the force feedback signal to system timely and accurately,predicting algorithm selects the particle filter algorithm,the algorithm and simulation,results show that the prediction accuracy can reach 97%.By plantar pressure signal,the establishment of plantar pressure and the relationship between Angle of lower limb rehabilitation robot joints,solving the lower limb joint angles,reusing reverse kinematic position at the end of the lower limb joint kinematics model is set up,using iteration algorithm to estimate lower limb movement,so that the system access to timely and accurate position feedback signal.The prediction model is verified by simulation and experimental data respectively.The results show that the prediction accuracy is 97.3% and the steady-state recognition time is 199 milliseconds.Based on the combination of predictive model and force position hybrid control,an active training control algorithm for lower limb rehabilitation robot with predictive feedback and force position hybrid control was established.Simulation results show that the algorithm can effectively improve the speed of motion control.Figure Fifty-five;Table Twenty-nine;Reference Fifty-six...