A precise BP neural network-based online model predictive control strategy for die forging hydraulic press machine

• Published in: Neural computing & applications Vol. 29; no. 9; pp. 585 - 596
• Main Authors: Lin, Y. C., Chen, Dong-Dong, Chen, Ming-Song, Chen, Xiao-Min, Li, Jia
• Format: Journal Article
• Language: English
• Published: London Springer London 01.05.2018; Springer Nature B.V
• Subjects: Artificial Intelligence; Back propagation; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Cylinders; Data Mining and Knowledge Discovery; Die forging; Hydraulic presses; Image Processing and Computer Vision; Mathematical models; Neural networks; Nonlinearity; Original Article; Predictive control; Probability and Statistics in Computer Science; Proportional integral derivative; Strategy; Forging process; Model predictive control; BP neural networks

The time variance and nonlinearity of forging processes pose great challenges to high-quality production. In this study, a one-step-ahead model predictive control (MPC) strategy based on backpropagation (BP) neural network is proposed for the precise forging processes. Two online updated BP neural n...