Research on fuzzy PID optimization control for continuous casting crystallizer liquid level based on BP neural network

According to the influence of stopper and drawing speed on the online control of continuous casting crystallizer liquid level, an optimization algorithm combining BP neural network and fuzzy PID has been proposed. Considering the control model, BP neural network is used to quantify the influencing factors of the molten metal inflow to the position of the stopper, and then fuzzy PID is used to correlate the liquid level in the crystallizer with the correction amount of the stopper position, the drawing speed, and other parameters. PID parameters can be adjusted online to achieve optimal control of the liquid level in the crystallizer. Simulation shows that the BP neural network and fuzzy PID can avoid the overshoot of the continuous casting crystallizer level control, make the level fluctuation stabilize in a short time, and reduce the deviation and the influence of uncertain disturbances such as stopper and drawing speed.