Adaptive Field Device Control Code for Self-Learning Calibration
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Solution Overview
Problem
Industrial controllers, such as PLCs, face challenges in accurately adjusting control parameters due to manufacturing tolerances and component wear, leading to cumbersome and labor-intensive calibration processes, which can result in suboptimal performance and the need for frequent recalibration.
Innovation Solution
A method and system for deploying self-optimizing functions on target field devices, utilizing machine learning models like neural networks to automatically optimize tuneable parameters through real-time learning, where a computation graph is generated to convert functions into machine executable code specific to the device, enabling iterative optimization and reducing the need for manual calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual calibration is performed to adjust control parameters, then manufacturing precision is improved, but productivity deteriorates due to labor-intensive processes and frequent recalibration
Solution Approach 1:
The control system automatically performs calibration by executing calibration routines that measure actual system behavior, compare it with expected behavior, and adjust parameters without human intervention. The system self-diagnoses and self-corrects parameter deviations, eliminating the need for manual operator involvement in the calibration process.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the technical system is fed back to the control unit. The control unit compares actual system performance with model predictions and automatically adjusts parameters based on the deviation, enabling real-time adaptive calibration that maintains precision without manual intervention.
2Manufacturing precision
If manual calibration is performed to adjust control parameters, then manufacturing precision is improved, but loss of time worsens due to frequent recalibration needs
Solution Approach 1:
The adaptive calibration system operates continuously in the background during normal system operation. Instead of periodic manual recalibration that stops production, the system continuously monitors performance and adjusts parameters in real-time, ensuring uninterrupted productive action while maintaining precision.
Solution Approach 2:
The system performs automatic recalibration without requiring production stoppages or human intervention. The control unit autonomously executes calibration routines and adjusts parameters during operation, eliminating time loss associated with manual recalibration procedures and production downtime.
3Measurement precision
If complex model parameters are adjusted to match real system properties, then measurement precision is improved, but device complexity worsens due to multiple tuneable parameters
Solution Approach 1:
The system automatically determines optimal parameter values through self-calibration routines. Instead of requiring users to manually configure multiple complex parameters, the control unit autonomously measures system characteristics and computes appropriate parameter settings, reducing the perceived complexity for the user while maintaining high model accuracy.
Solution Approach 2:
The system dynamically adjusts parameter values based on actual system behavior rather than using fixed manufacturer defaults. Through adaptive parameter tuning, the system transforms a complex static parameter configuration problem into a simpler dynamic adjustment process where parameters automatically adapt to match real system properties.
4Adaptability or versatility
If adaptive self-learning control is deployed, then adaptability is improved, but device complexity worsens due to machine learning model integration
Solution Approach 1:
The control unit serves multiple functions: it executes standard control routines, performs adaptive calibration, runs machine learning inference, and manages model updates. By consolidating these diverse functions into a single multi-functional control device, the system achieves high adaptability without proportionally increasing overall device complexity.
Solution Approach 2:
A computation graph serves as an intermediary structure that bridges the machine learning model and the industrial control system. The computation graph translates complex model operations into executable control instructions, enabling adaptive self-learning functionality while maintaining a manageable control system architecture through structured intermediate representation.
Data Source
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AI summary
A method for deploying and executing self-optimizing functions on a target field device (TFD), the method comprising the steps of providing (S1) a set of functions, f, having at least one tuneable parameter, θ; deriving (S2) automatically from the provided set of functions, f, an additional set of functions used to optimize the tuneable parameters, θ; converting (S3) both sets of functions into a machine executable code specific to said target field device (TFD); and deploying (S4) and executing the converted machine executable code on said target field device (TFD).