CD Web MPC Spatial Tuning for Multi-Array Robustness
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Solution Overview
Problem
Tuning model predictive control (MPC) or other model-based controllers for cross-direction (CD) control in web manufacturing or processing systems is challenging due to non-intuitive tuning parameters and the need for advanced control theory understanding.
Innovation Solution
The method involves spatial and temporal tuning of model-based controllers to achieve robust performance. Spatial tuning identifies weighting matrices that suppress frequency components in actuator profiles, while temporal tuning adjusts parameters to ensure desired temporal performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If automated tuning algorithms relying on robust control theory are used, then tuning can be automated, but the algorithms require a user with an advanced understanding of control theory and involve abstract parameters
Solution Approach 1:
The patent introduces an intermediary translation layer that converts abstract control theory parameters into intuitive process-specific parameters. The system acts as a mediator between automated robust control algorithms and end users, translating complex mathematical parameters into meaningful process variables that operators can understand and adjust without requiring advanced control theory knowledge.
Solution Approach 2:
The patent transforms abstract control parameters into intuitive process parameters through systematic parameter changes. By redefining tuning parameters in terms of process-specific characteristics rather than mathematical abstractions, the system maintains automated tuning capabilities while making the interface accessible to operators with basic process knowledge.
2Reliability
If model predictive control is used for CD control, then control performance can be improved, but the tuning parameters are not intuitive and trial and error tuning attempts can be time-consuming
Solution Approach 1:
The patent implements preliminary action by pre-configuring intuitive process-specific parameters that directly map to controller behavior. Instead of requiring operators to perform trial-and-error tuning of abstract parameters, the system provides pre-defined parameter sets that can be selected and adjusted based on process characteristics, significantly reducing tuning time while maintaining MPC performance benefits.
3Adaptability or versatility
If multiple actuator arrays are used in web manufacturing systems, then control capability is enhanced, but the complexity of tuning the controller increases
Solution Approach 1:
The patent applies segmentation by breaking down the complex multi-array controller into manageable process-specific parameter groups. Each actuator array is associated with intuitive parameters that reflect its specific process role, allowing operators to tune each segment independently using process knowledge rather than dealing with the overall system complexity.
Data Source
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AI summary
A method includes obtaining (902) one or more models (304) associated with a model-based controller (104, 306) in an industrial process (100, 302) having multiple actuator arrays (114, 116, 118, 120) and performing (906) spatial tuning of the controller. The spatial tuning includes identifying weighting matrices that suppress one or more frequency components in actuator profiles of the actuator arrays. The spatial tuning could also include finding (908) a worst-case cutoff frequency over all output channels for each process input, designing (910) the weighting matrices to penalize higher-frequency actuator variability based on the model(s) and the cutoff frequencies, and finding (912) a multiplier for a spatial frequency weighted actuator variability term in a function that guarantees robust spatial stability. The controller could be configured to use a function during control of the industrial process, where a change to one or more terms of the function alters operation of the controller and the industrial process and at least one term is based on the weighting matrices.