Neuro-Fuzzy System for Compensating Slow Disturbances in Adaptive Mold Level Control

Abstract

Good slow disturbances attenuation in a mold level control with stopper rod is very important for avoiding several product defects and keeping down casting interruptions. The aim of this work is to improve the accuracy of the diagnosis and compensation of an adaptive mold level control method for slow disturbances related to changes of stopper rod. The advantages offered by the architecture, called Adaptive-Network-based Fuzzy Inference System, were used for training a previous model. This allowed learning based on the process data from a steel cast case study, representing all intensity levels of valve erosion and clogging. The developed model has high accuracy in its functional relationship between two compact input variables and the compensation coefficient of the valve gain variations. The future implementation of this proposal will consider a combined training of the model, which would be very convenient for maintaining good accuracy in the Fuzzy Inference System using new data from the process.

A Model-Based Predictive Controller of the Level of Steel in the Mold with Disturbances Using a Repetitive Structure

“…The slide gate valve is located next to the outlet of the tundish and is responsible for transporting the flow of steel into the mold, through the submerged valve. The flow of steel into the slide gate valve according to [5] can be written as V ns = 2gh. Thus, from Equation (1), we obtain Equation (2):…”

“…The quality of the steel depends on the degree to which a predetermined liquid steel level can be kept within a specific range (setpoint) during the many stages of the process. The steel level of the mold in continuous casters is affected by nonlinearities and disturbances, such as bulging, mold oscillations, valve erosion, argon injection, and clogging/unclogging [5].…”

“…Thus, to minimize its effects, the control system must be robust so that it functions in the best possible way, even with the variation in steel flow. The effect of clogging has also been studied extensively from a metallurgical point of view [5,16].…”

“…The authors [5] used an adaptive architecture with a nonlinear proportional-integral (PI), based on a fuzzy inference system (ANFIS), and online identification to compensate for the slow disturbances (erosion and clogging) in mold level. To stabilize the mold level in consideration of the clogging and unclogging effects, a researcher [13] designed a fuzzy PID controller with a nonlinear compensation term.…”

Control of a Variable Blade Pitch Wind Turbine Subject to Gust Wind and Actuators Saturation

“…Then, an adaptive backstepping tracking-control scheme is developed. Other advanced methods like output feedback [27,28], neuro-fuzzy [29], genetic algorithm [30], model predictive [31], and interval type-2 fuzzy systems [32] were used.…”