Active Control Of Lower Extremity Exoskeleton Joints Without Sensory Model

Lower extremity exoskeleton rehabilitation robot is a kind of equipment used to assist patients with motor dysfunction in lower extremity rehabilitation training.In recent years,the number of patients requiring rehabilitation training has increased due to aging problems in China,which has caused extensive attention to lower extremity exoskeleton rehabilitation robot.Its rehabilitation training mode can be divided into passive training and non-passive training(assisted active training,full active training and resistance resistance active training)according to the number of patients' active output during the training process.These rehabilitation training modes are applicable to different rehabilitation training periods.This paper studies the non-passive training method suitable for the middle and late stage of rehabilitation training.Its implementation relies on human-machine interaction torque information.The current methods for measuring human-machine interaction torque include torque sensor method,biosensor method such as EEG,EMG,and observer method without torque sensor.The main problems of the torque sensor method are high cost,large volume,and difficulty in installation.The biosensor methods such as EEG and EMG also have high cost problems,and are susceptible to interference,and the requirements for the use environment are relatively high.This thesis aims at the lower extremity exoskeleton rehabilitation robot,in order to improve the freedom of movement and active movement awareness during training,and to reduce system complexity and development cost.Firstly,a torque observer is built by means of non-sensing model to realize the perception of the human-machine interaction torque.Then,based on it,the reference trajectory adaptive control strategy is designed to reduce the human-machine interaction torque during the training process,and to improve the freedom of movement and the awareness of active movement.The main work and results of this paper are as follows.On the basis of fully analyzing the characteristics of human-machine interaction information in the lower extremity exoskeleton rehabilitation robot system,it is decided to use the human-machine interaction torque as the reference basis for the design of the active training control strategy.Aiming at the problems existing in the above-mentioned human-computer interaction torque measurement method,this paper proposes a measurement method that does not require a torque sensor according to the characteristics of the system.The method acquires human-machine interaction torque information through a torque observer established by an exoskeleton dynamics model and a motor electromagnetic torque model.In order to verify the feasibility of the torque observer algorithm,this paper built the Simulink simulation platform to verify its principle and performance.Based on the human-machine interaction torque observer,this paper proposes a reference trajectory adaptive active control algorithm,which can modify the parameters of the joint motion curve within a certain range by minimizing the human-machine interaction torque.It can respond to the patient's motion intention in terms of humancomputer interaction torque or reference trajectory,match the patient's exercise ability,improve the patient's freedom of movement and the sense of participation and the awareness of active movement during rehabilitation training,thereby achieving the purpose of improving the efficiency of rehabilitation training and accelerating the progress of rehabilitation training.Based on the simulation verification of the human-machine interaction torque observer algorithm and the active training control algorithm,the rehabilitation experiment platform of the lower extremity exoskeleton is built and the actual system physics experiment is carried out.The experimental results further verify the feasibility of the above algorithm.