Patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method

Abstract

The invention discloses a patient movement demand-based assistance lower limb rehabilitation robot self-adaptation control method. By collecting the joint angle and joint angle speed signal of the lower limb of a patient in real time, the expected track self-adaptation tracking control is realized by a robustness variable-structure control method; then, by using a man-machine dynamics system model, the rehabilitation degree and the active movement ability of the patient are studied in real time by using a RBF (Radial Basis Function) neural network; the forward feed assistance of a lower limb rehabilitation robot is further estimated; next, the real-time assistance of the robot is subjected to self-adaptation attenuation according to the track tracking errors; the continuous self-adaptation patient rehabilitation demand-based assistance control is realized; finally, the tracks subjected to the patient rehabilitation demand-based assistance self-adaptation control correction are input into a lower limb rehabilitation robot joint movement controller; the on-line movement is performed; and the continuous and seamless patient rehabilitation demand-based assistance lower limb rehabilitation robot self-adaptation control is realized.

Description

technical field [0001] The invention relates to robot control technology, in particular to an adaptive control method for a lower limb rehabilitation robot. Background technique [0002] The number of patients with lower extremity motor dysfunction caused by central nervous system diseases such as spinal injury and stroke is increasing sharply, seriously endangering human health. Weight-loss walking training is one of the important means of walking rehabilitation for patients with such diseases, and a large number of clinical studies have confirmed its effectiveness. For this reason, combining robotics technology with rehabilitation medicine, developing an intelligent lower limb rehabilitation robot to replace nurses to complete gait movement training for hemiplegic patients can significantly improve the rehabilitation level of patients and reduce the labor intensity of nurses. At present, many people at home and abroad are carrying out research work on rehabilitation robot...

AI Technical Summary

Problems solved by technology

However, the EMG signal has strong ambiguity, poor anti-interference, and low control accuracy. The impedance model also obtains auxiliary force by sacrificing position deviation.

[0005] In summary, impedance control is not suitable for the support phase. Bioelectrical signal control divides the auxiliary control into the patient-driven part and the machine-driven part, and sets the control mode as a discrete rehabilitation training mode, which cannot provide continuous and seamless rehabilitation according to the patient's needs. It is difficult to adapt to patients with different gait cycles and different recovery periods

The current control methods of rehabilitation equipment can not meet the needs of intelligent and adaptive control.

It is difficult to realize the real sense of adaptive control according to the patient's motion rehabilitation needs

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