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

Abstract

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 networks, predictive neural network (PNN) and control neural network (CNN), are developed to accurately control the die forging hydraulic press machine. The PNN and CNN are utilized to predict the output (the velocity of upper die) and determine the input (the oil pressure of driven cylinders), respectively. The weights of neural networks are initially trained offline and then updated online according to an error backpropagation algorithm. In the proposed control strategy, only the input and output are required, which makes the forging process easy to be controlled. In addition, because of the generalized ability and adaptability of neural networks, the proposed predictive controller can well deal with the time variance and nonlinearity of forging process. Two forging experiments demonstrate the feasibility and effectiveness of the proposed strategy. Moreover, comparing the proposed MPC strategy with the traditional MPC approach and PID controller, it can be found that the proposed MPC strategy is the most effective control approach for the practical forging process.