下肢外骨骼助行机器人神经网络自适应滑模控制研究.pdf

Modeling and Simulation 建模与仿真, 2022, 11(2), 288-296
Published Online March 2022 in Hans. de-
vice. It can assist the human body to perform human-like gait movement. The system needs a good
track tracking effect. To make the tracking accuracy and stability of the system better, a dynamic
model was established by Lagrangian method, and the RBF neural network adaptive sliding mode
control algorithm was studied and analyzed. Through MATLAB-Simulink simulation, the sliding
mode algorithm and RBF neural network adaptive sliding mode control algorithm are compared
and analyzed. Through simulation, the angle and speed control curves of the hip and knee joints
under the two algorithms are obtained. The torque curve is also obtained. On this basis, the posi-
tion tracking errors of the two algorithms are compared. Experimental results show that RBF
neural network adaptive sliding mode control can improve the chattering of sliding mode control,
and improve the control accuracy and anti-interference ability of the system.

Keywords
Wearable Lower Limb Exoskeleton Walking Aid Robot, Dynamic Model, Tracking, RBF Neural Network


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