Cloud Tuning Analytics for Industrial PID Loop Optimization
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Solution Overview
Problem
Industrial PID control loop tuning systems face challenges in optimizing controller gain values, requiring manual trial-and-error methods that are time-consuming and lack precision, leading to suboptimal performance and stability issues.
Innovation Solution
A cloud-based control loop tuning system that collects industrial data, generates a gain correlation model, and determines optimal controller gain values using correlation analytics, leveraging big data analysis and machine learning to automate the tuning process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual trial-and-error methods are used for PID control loop tuning, then the system can be tuned without additional infrastructure, but the tuning process is time-consuming and lacks precision
Solution Approach 1:
The patent replaces manual mechanical trial-and-error tuning with an automated electronic system that collects process data, analyzes it using algorithms, and automatically generates optimized PID controller gain values. This substitution eliminates the time-consuming manual process while providing precise, data-driven tuning recommendations.
Solution Approach 2:
The system enables self-service tuning by automatically collecting process data, analyzing performance metrics, and generating optimized controller parameters without requiring manual intervention from control engineers. The automated analysis and recommendation engine provides precise tuning values based on actual process behavior.
2Extent of automation
If a cloud-based control loop tuning system is implemented, then automated tuning with high precision is achieved, but system complexity and infrastructure requirements increase
Solution Approach 1:
The patent introduces a cloud-based intermediary platform that acts as a mediator between the industrial control system and the tuning analysis engine. This intermediary collects data from multiple sources, performs centralized analysis, and returns optimized parameters, thereby automating the tuning process while managing system complexity through a dedicated intermediate layer.
Solution Approach 2:
The cloud-based tuning system provides universal functionality by serving multiple control loops and processes through a single centralized platform. The system can analyze various process types, generate optimized parameters for different controller configurations, and provide automated tuning services across diverse industrial applications, reducing overall system complexity through consolidation.
Data Source
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AI summary
A control loop tuning system executing on a cloud platform facilitate remote control system analysis and generation of suitable controller gains for a given closed-loop control application. The system leverages cloud-side analytics and a gain correlation model generated based on historical data collected from the industrial control system and maintained on cloud storage. The gain correlation model creates a virtual association between controller gains and process variables based on operational and configuration data collected from the industrial control system. The system then applies iterative analytics to the model to converge on a set of controller gains determined to satisfy an optimization criterion. The recommended controller gains are then provided to a client device for review and implementation in the real system controller.