Human-Like Robust Adaptive PD Based Human Gait Tracking for Exoskeleton Robot

2021 ◽  
Vol 33 (1) ◽  
pp. 88-96
Author(s):  
Aihui Wang ◽  
Ningning Hu ◽  
Jun Yu ◽  
Junlan Lu ◽  
Yifei Ge ◽  
...  

For patients with dyskinesias caused by central nervous system diseases such as stroke, in the early stage of rehabilitation training, lower limb rehabilitation robots are used to provide passive rehabilitation training. This paper proposed a human-like robust adaptive PD control strategy of the exoskeleton robot based on healthy human gait data. When the error disturbance is bounded, a human-like robust adaptive PD control strategy is designed, which not only enables the rehabilitation exoskeleton robot to quickly track the human gait trajectory obtained through the 3D NOKOV motion capture system, but also can well identify the structural parameters of the system and avoid excessively initial output torque for the robot. MATLAB simulation verifies that the proposed method has a better performance to realize tracking the experimental trajectory of human movement and anti-interference ability under the condition of ensuring global stability for a lower limb rehabilitation exoskeleton robot.

2021 ◽  
Vol 7 ◽  
pp. e394
Author(s):  
Ningning Hu ◽  
Aihui Wang ◽  
Yuanhang Wu

The combination of biomedical engineering and robotics engineering brings hope of rehabilitation to patients with lower limb movement disorders caused by diseases of the central nervous system. For the comfort during passive training, anti-interference and the convergence speed of tracking the desired trajectory, this paper analyzes human body movement mechanism and proposes a robust adaptive PD-like control of the lower limb exoskeleton robot based on healthy human gait data. In the case of bounded error perturbation, MATLAB simulation verifies that the proposed method can ensure the global stability by introducing an S-curve function to make the design robust adaptive PD-like control. This control strategy allows the lower limb rehabilitation robot to track the human gait trajectory obtained through the motion capture system more quickly, and avoids excessive initial output torque. Finally, the angle similarity function is used to objectively evaluate the human body for wearing the robot comfortably.


Author(s):  
Yanlin Wang ◽  
Keyi Wang ◽  
Zixing Zhang ◽  
Zongjun Mo

This paper aims to solve the problems of the existing limbs rehabilitation robots in terms of configuration limitations, human-machine compatibility, multimodal rehabilitation training. In addition, the control method of the cable tension of cable drive unit (CDU) loading system is studied to improve loading accuracy of cable tension and safety of the rehabilitation training robot. The novelty of this work is to propose a compound correction controller that can not only ensure the tracking accuracy of the cable-driven lower limb rehabilitation robot (CDLR) but also effectively improve the force loading accuracy of the cable tension force. Hence, this paper proposes a CDLR that can realize the active training mode, passive training mode, and assistive training mode. Firstly, the structure and working principle of CDLR is introduced. The dynamic model of the CDU loading system is established and the frequency characteristic of the CDU loading system is analyzed. In order to improve the loading accuracy and response speed of the CDU loading system, a compound correction controller is designed based on the frequency characteristics of the CDU loading system. Finally, the active force servo control experiment and the passive force servo control experiment of the CDU loading system are carried out on the experimental platform. The experimental results show that the compound correction control strategy can meet the requirements of lower limb rehabilitation training in the active force servo control experiment; the compound correction control strategy can significantly improve the loading precision and dynamic performance of the system in the passive force servo control experiment. That is, the compound correction control strategy can meet the requirements of lower limb rehabilitation training. The results provide a basis for the whole robot experiment and human-machine experiments and improve the stability of the CDLR system and patient safety.


2021 ◽  
pp. 1-44
Author(s):  
Chennan Yu ◽  
Jun Ye ◽  
Jiangming Jia ◽  
Xiong Zhao ◽  
Zhiwei Chen ◽  
...  

Abstract A foot-driven rehabilitation mechanism is suitable for home healthcare due to its advantages of simplicity, effectiveness, small size, and low price. However, most of the existing studies on lower limb rehabilitation movement only consider the trajectory of the ankle joint and ignore the influence of its posture angle, which makes it difficult to ensure the rotation requirements of the ankle joint and achieve a better rehabilitation effect. Aiming at the shortcomings of the current research, this paper proposes a new single degree-of-freedom (DOF) configuration that uses a noncircular gear train to constrain the three revolute joints (3R) open-chain linkage and expounds its dimensional synthesis method. Then, a parameter optimization model of the mechanism is established, and the genetic algorithm is used to optimize the mechanism parameters. According to the eight groups of key poses and position points of the ankle joint and the toe, the different configurations of the rehabilitation mechanism are synthesized and compared, and it is concluded that the newly proposed 3R open-chain noncircular gear-linkage mechanism exhibits better performance. Finally, combined with the requirements of rehabilitation training, a lower limb rehabilitation training device is designed based on this new configuration, and a prototype is developed and tested. The test results show that the device can meet the requirements of the key position points and posture angles of the ankle joint and the toe and verify the correctness of the proposed dimensional synthesis and optimization methods.


2019 ◽  
Vol 33 (11) ◽  
pp. 5461-5472 ◽  
Author(s):  
Yan-lin Wang ◽  
Ke-yi Wang ◽  
Wan-li Wang ◽  
Peng-cheng Yin ◽  
Zhuang Han

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