Dynamic modeling and optimization of an eight bar stamping mechanism based on RBF neural network PID control - Ma, Dongsheng; Li, Juchen (Frontiers in Mechanical Engineering, 2024)
The target of this paper is to improve the motion accuracy and stability of the eight bar stamping mechanism to satisfy the high needs of modern industrial manufacturing. A radial basis function neural network is introduced to improve traditional Proportional-Integral-Derivative control techniques. The improved Proportional-Integrated-Derivative technology obtained through integration is applied to the modeling and optimization of eight bar stamping mechanisms. Comparing the improved control technology, the experiment showed that the peak time and adjustment time of the improved technology were 0.516s and 1.038s, respectively, which are better than the comparative control technology. In addition, in the comparative analysis of the eight bar stamping mechanism, the proposed architecture scored 9.3 points in operational efficiency, which is significantly greater than the comparative architecture. The above explanation shows that the Proportional-Integrated-Derivative control strategy built on Radial Basis Function provides an effective means for dynamic modeling and optimization of eight bar stamping mechanisms, and also brings practicability to the stimulation of industrial manufacturing.