Predictive Control for Cold Rolling Mill Gauge and Flatness
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Solution Overview
Problem
Current metal rolling mills face challenges in achieving robust and high-performance thickness control due to varying time delays, non-linearities, and internal disturbances, leading to suboptimal gauge and flatness control, especially during acceleration and deceleration states.
Innovation Solution
A model-based multi-variable predictive control system is developed for 4-hi non-reversible single-stand metal rolling mills, utilizing a model predictive controller (MPC) that adjusts roll gap, roll force, mill speed, and strip tension to execute automatic gauge control (AGC) and automatic flatness control (AFC), improving control accuracy and reducing thickness and flatness deviations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If PID-based closed loop control is used for gauge and flatness control, then the control system is simple and reliable, but the product optimization time is high and control accuracy is insufficient
Solution Approach 1:
The control system performs preliminary actions by predicting future thickness and flatness deviations using a process model before they actually occur. The model-based predictive controller calculates optimal control moves in advance based on predicted disturbances and plant dynamics, allowing the system to proactively compensate for upcoming deviations rather than reacting after they occur, thereby reducing optimization time while improving accuracy.
Solution Approach 2:
The invention implements dynamic control by using a model-based predictive controller that continuously adapts control actions based on real-time measurements of thickness, flatness, and disturbance variables. The controller dynamically adjusts roll gap, roll force, and other parameters according to predicted future states, enabling the system to respond differently to various operating conditions including acceleration and deceleration states, thereby improving both accuracy and response time.
2Manufacturing precision
If traditional control approaches are used, then the control system is easier to implement, but thickness and flatness deviations are not significantly reduced
Solution Approach 1:
The invention introduces a model-based predictive controller as an intermediary between the sensors (measuring thickness, flatness, and disturbances) and the actuators (roll gap control, roll force control). This intermediary processes measurements through a process model to predict future deviations and calculates optimal control moves, serving as a sophisticated mediator that translates raw measurements into precise control actions, thereby significantly improving thickness uniformity and flatness control.
Solution Approach 2:
The control system implements enhanced feedback by continuously measuring thickness, flatness, and disturbance variables (entry thickness, entry speed, roll eccentricity, thermal growth, wear) and using these measurements to update the predictive model. The controller uses this feedback loop to continuously refine predictions and adjust control moves, ensuring high manufacturing precision while managing system complexity through systematic feedback integration.
3Reliability
If the control system does not account for varying time delays and non-linearities, then the control algorithm is simpler, but disturbance rejection is insufficient
Solution Approach 1:
The predictive controller performs preliminary actions by using the process model to predict the effects of disturbances and control moves before implementing them. The model accounts for varying time delays and non-linearities by incorporating plant dynamics and disturbance models, allowing the controller to pre-calculate optimal control moves that compensate for these complexities, thereby improving disturbance rejection while managing algorithm complexity through structured modeling.
Solution Approach 2:
The control algorithm manages complexity by using parameter changes in a systematic way. The model-based predictive controller uses adjustable parameters such as prediction horizon, control horizon, and weighting matrices to balance disturbance rejection performance with computational complexity. The controller adapts these parameters to account for varying time delays and non-linearities without requiring overly complex algorithms, achieving reliable disturbance rejection through parameter optimization.
Data Source
AI summary
A control system employs a model-based multi-variable predictive control for cold rolling mills to improve sheet thickness uniformity to meet or exceed specifications in flatness. Sheet metal thickness and flatness deviations from standard requirements are significantly reduced with attendant improved control accuracy as compared to traditional control approaches that use PID based closed loop controls. The control system is particularly suited for control of 4-hi non-reversible single-stand metal rolling mills. The mill stand has a first work roll and a second work roll respectively positioned between a first back up roll and a second back up roll. A plurality of sensors measures and acquires property data of the sheet of material. A model predictive controller manipulates actuators to regulate thickness and flatness. The controller executes automatic gauge control, which is machine direction metal control, and automatic flatness control, which is cross direction metal sheet control, as metal sheet is rolled.