Abstract
In this study, a humanoid prototype of 2-DOF (degrees of freedom) lower limb exoskeleton is introduced to evaluate the wearable comfortable effect between person and exoskeleton. To improve the detection accuracy of the human-robot interaction torque, a BPNN (backpropagation neural networks) is proposed to estimate this interaction force and to compensate for the measurement error of the 3D-force/torque sensor. Meanwhile, the backstepping controller is designed to realize the exoskeleton's passive position control, which means that the person passively adapts to the exoskeleton. On the other hand, a variable admittance controller is used to implement the exoskeleton's active follow-up control, which means that the person's motion is motivated by his/her intention and the exoskeleton control tries best to improve the human-robot wearable comfortable performance. To improve the wearable comfortable effect, serval regular gait tasks with different admittance parameters and step frequencies are statistically performed to obtain the optimal admittance control parameters. Finally, the BPNN compensation algorithm and two controllers are verified by the experimental exoskeleton prototype with human-robot cooperative motion.
Highlights
In recent decades, the wearable robot has been widely applied in the field of medical rehabilitation engineering
The lower limb exoskeleton was driven by a hydraulic actuator and series elastic actuators (SEAs) in Refs. [3] and [4], respectively
5.1 Stability Evaluation with Different Step Frequency For the error correction experiment of the 3-D force sensor, the 12 BP neural networks (Thigh-Down-X, Thigh-Down-Y, Thigh-Down-Z, Thigh-Up-X, ThighUp-Y, Thigh-Up-Z, Shank-Down-X, Shank-Down-Y, Shank-Down-Z, Shank-Up-X, Shank-Up-Y, Shank-Up-Z) with the structure as shown in Figure 5 had been trained by the sampling set
Summary
The wearable robot has been widely applied in the field of medical rehabilitation engineering. An admittance control based on human stiffness estimator was adopted for the exoskeleton robot, and a variable admittance control based on online neural network training is proposed in Ref. Gui et al [25] presented a practical and adaptive method to estimate active joint torque using electromyography (EMG) signals for a custom lower limb robotic exoskeleton with two DOFs. Zhuang et al [26] presented an EMG-based admittance controller to realize both synchronized and stable human-robot interaction. In the exoskeleton active mode, a variable admittance controller is used to implement the exoskeleton’s follow-up control In this condition, the person’s motion is motivated by his/ her intention, and the exoskeleton is driven by the admittance controller to improve the human-robot wearable comfortable performance.
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