Inferential Thickness Control Using Kalman Filter
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Solution Overview
Problem
The metal rolling process faces challenges in controlling sheet thickness due to varying time delays, non-linearities, and internal disturbances like roll eccentricity, thermal growth, and thermo-mechanical wear, lacking a coordinated and systematic approach for effective thickness control.
Innovation Solution
A rolled sheet metal mill controller utilizing multiple models, including a rolling model, gap control model, and main drive model, coupled with a Kalman filter to inferentially sense and compensate for internal disturbances, providing closed-loop control of sheet metal thickness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple models and Kalman filter are used to compensate for internal disturbances, then thickness control accuracy is improved, but device complexity increases
Solution Approach 1:
The controller is divided into multiple functional models: rolling model, gap control model, and main drive model. Each model handles specific aspects of the rolling process, allowing complex control tasks to be broken down into manageable segments that can be processed independently and then integrated through the Kalman filter.
Solution Approach 2:
The Kalman filter acts as an intermediary that processes information from multiple models and sensors, estimating internal disturbances (roll eccentricity, thermal growth, wear) and providing compensated control signals. This mediator coordinates the interaction between multiple models and the final control output, managing system complexity.
2Reliability
If time varying delay is modeled for sheet metal thickness, then control robustness is improved, but measurement precision requirements increase
Solution Approach 1:
The system performs preliminary estimation of the time-varying delay using the main drive model and roll speed information before the thickness measurement is fully processed. This allows the controller to anticipate and compensate for delay effects in advance, reducing the impact of measurement precision limitations.
Solution Approach 2:
The Kalman filter continuously updates the delay model using feedback from actual thickness measurements and compares it with predicted values from the rolling model. This feedback mechanism allows the system to adapt to changing conditions and maintain robustness even when measurement precision varies.
Data Source
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AI summary
A rolled sheet metal mill (910) controller (900) for controlling thickness of sheet metal (922) produced by rolls of the mill (910), the controller (900) comprising one or more processors and code stored on media readable by the one or more processors to control the thickness of the produced sheet metal (922), the controller (900) including an input (940) coupled to receive multiple measured mill parameters including produced sheet metal thickness h that is time delayed from the production of the sheet metal (922), multiple models (935) of the sheet metal mill (910), wherein the sheet metal thickness is modeled as an input varying delay, and at least one internal disturbance model based on one or more of the multiple measured parameters coupled to the input (940), a Kalman filter KF based on the multiple models (935), and an output (955) coupled to control a gap between the rolls that produce the rolled sheet metal (922).