CD Web MPC Tuning for Robust Stability Across Actuator Arrays
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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 with multiple actuator beams is challenging due to non-intuitive tuning parameters and the need for advanced control theory understanding, making automated tuning difficult and time-consuming.
Innovation Solution
The method involves separate spatial and temporal tuning of MPC controllers using intuitive process model quality and desired temporal control performance information, allowing non-experts to provide minimal input for automated tuning, with spatial tuning reducing steady-state variability and temporal tuning adjusting parameters for smooth actuator movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If MPC or model-based control is used for CD control with multiple actuator beams, then control performance and stability can be improved, but the tuning complexity and difficulty increase significantly
Solution Approach 1:
The patent segments the tuning process into two distinct phases: spatial tuning (adjusting weighting matrices Q and S for different spatial locations and actuator beams) and temporal tuning (adjusting temporal parameters like prediction horizon and control horizon). This segmentation allows experts to focus on spatial distribution characteristics while automated algorithms handle temporal aspects, reducing overall tuning complexity while maintaining control stability.
Solution Approach 2:
The patent introduces automated tuning algorithms as intermediaries between the control engineer and the complex MPC parameters. These algorithms use process identification data and performance specifications to automatically determine optimal weighting matrices and temporal parameters, acting as a mediator that translates high-level requirements into detailed controller settings without requiring deep control theory expertise.
2Productivity
If automated tuning algorithms relying on robust control theory are used, then tuning efficiency can be improved, but the requirement for advanced control theory understanding increases
Solution Approach 1:
The patent implements self-service tuning where the system automatically identifies process characteristics from operational data and uses this information to configure appropriate weighting matrices and temporal parameters. The automated algorithms serve themselves by using process identification results to guide the tuning process, reducing the need for expert intervention while maintaining high tuning efficiency.
Solution Approach 2:
The patent changes the parameter representation from abstract robust control theory parameters to intuitive process-specific parameters such as spatial weighting factors for different actuator beams and temporal horizons based on process dynamics. This parameter transformation allows automated algorithms to work efficiently while presenting results in terms that are easier for practitioners to understand and validate.
3Adaptability or versatility
If trial and error tuning attempts are used, then flexibility in achieving desired performance can be improved, but the time required for tuning increases significantly
Solution Approach 1:
The patent performs preliminary process identification to extract key dynamic characteristics and spatial distribution patterns before the actual tuning process. This preliminary action provides a head start by establishing baseline weighting matrices and temporal parameters that are already adapted to the specific process, eliminating the need for extensive trial and error while preserving flexibility through subsequent refinement steps.
Solution Approach 2:
The patent implements iterative feedback loops where the automated tuning algorithms evaluate controller performance against specified objectives and automatically adjust weighting matrices and temporal parameters. This feedback mechanism replaces manual trial and error with systematic automated adjustments, reducing tuning time while maintaining the flexibility to achieve desired performance through continuous refinement based on performance metrics.
Data Source
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AI summary
A method includes obtaining (902) a model (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 (914) temporal tuning of the controller. The temporal tuning includes adjusting one or more parameters of a multivariable filter (308) used to smooth reference trajectories of actuator profiles of the actuator arrays. The temporal tuning could also include obtaining (904) one or more uncertainty specifications for one or more temporal parameters of the model, obtaining (916) one or more overshoot limits for the actuator profiles, identifying (918) a minimum bound for profile trajectory tuning parameters, and identifying (920) one or more of the profile trajectory tuning parameters that minimize one or more measurement settling times without exceeding the one or more overshoot limits. The controller could be configured to use the adjusted parameter(s) during control of the industrial process such that the adjusting of the parameter(s) alters operation of the controller and the industrial process.