The hydraulic flexible robotic arm is affected by various dynamic potential energies such as friction,gravi-ty,and traction,resulting in complex changes in motion characteristics and significant positioning control errors.Therefore,a terminal positioning control method based on RBF neural network is proposed.Establish an RBF neural network architecture,using Gaussian functions as the connection function between the input layer and the fuzzy layer,and introducing factors such as friction,traction,and gravity to analyze its dynamic characteristics.Based on this con-straint,a nonlinear positioning control compensation method is designed to achieve high-quality positioning control by adjusting the balance threshold through multiple iterations.The experimental results demonstrate that the proposed method has high accuracy in positioning control,and the changes in the motion rotation angle and vertical angle curve of the controlled robotic arm are basically consistent with the target values,demonstrating excellent control per-formance.
维普
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