The invention discloses an
upper limb exoskeleton rehabilitation robot control method based on a radial basis neural network. The method includes the steps that a
human body upper limb musculoskeletal model is established;
upper limb muscle myoelectric signals and upper limb movement data are collected, the movement data are imported into the upper limb musculoskeletal model, upper
limb joint torque is obtained, the radial basis neural network is established and a neural
network model is given out; the patient movement intention is recognized, the joint angular speed is subjected to fusion analysis, the result is used for recognizing the training object joint stretching state, and the limb movement intention is determined; and myoelectric signals and joint angles in affected side
rehabilitation training are collected in real time, affected side joint torque is obtained through the neural network, joint torque needing to be compensated by an
exoskeleton mechanical arm is calculated,
myoelectric signal fatigue characteristics are analyzed, the compensation torque magnitude can be adjusted by classifying the degree of fatigue, and a
torque controller can be controlled to achieve the effect that an
upper limb rehabilitation robot assists patients in
rehabilitation training by combining the movement intention. By means of the
upper limb exoskeleton rehabilitation robot control method, the
rehabilitation training process is made to be more suitable for the patients, man-
machine interaction is strengthened, and the rehabilitation effect is improved.