Adaptive Process Control Using Cross-Site Neural Learning
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Solution Overview
Problem
Existing predictive controllers face challenges in obtaining process models, particularly nonlinear models, which drift over time due to changes in raw materials and machine wear, leading to mismatch issues. Additionally, reinforcement learning controllers struggle with generalizing past experiences to new situations and require high-efficiency expressions from high-dimensional inputs, while existing cloud-based solutions lack interactive knowledge learning between different production sites and are costly and inconvenient to maintain.
Innovation Solution
A process controller system that utilizes a deep neural network to extract and train process control data from a centralized data storage device, incorporating characteristic information such as production device parameters and loads, to predict and determine operation control instructions for production devices, enabling effective control policies for novel situations by leveraging data from multiple sites.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a process model is used in predictive control, then control actions can be based on predicted values, but the model drifts over time due to raw material changes and machine wear causing mismatch
Solution Approach 1:
The system transitions from static process models to dynamic adaptive modeling through reinforcement learning. The controller continuously learns and updates its policy based on real-time feedback from the production device, allowing the control strategy to adapt to changing conditions without requiring explicit model updates. This resolves the contradiction by making the control system dynamically adjust to model drift rather than relying on fixed model consistency.
Solution Approach 2:
The reinforcement learning controller performs self-learning and self-adjustment by automatically extracting features from process data and optimizing control policies without external intervention. The system serves itself by continuously improving its control strategy through experience accumulation, eliminating the need for manual model recalibration and maintaining reliability despite changing process conditions.
2Reliability
If reinforcement learning is used to solve model-free adaptive control, then control performance improves, but the ANN requires high-efficiency expressions from high-dimensional inputs which is difficult to realize
Solution Approach 1:
The system extracts only the most relevant features from high-dimensional process data using the deep neural network's feature extraction capability. Instead of processing all raw input dimensions, the network identifies and extracts key predictive features that are most important for control decisions. This reduces the effective dimensionality of the problem while maintaining the adaptive performance benefits of reinforcement learning.
Solution Approach 2:
The control system is segmented into distinct functional modules: data collection from multiple sources, feature extraction through deep neural networks, reinforcement learning policy optimization, and control instruction generation. This segmentation allows each module to specialize in specific tasks, reducing the overall complexity of the ANN processing by breaking down the high-dimensional input problem into manageable processing stages.
3Ease of operation
If cloud platforms are used for data interaction, then information interaction becomes convenient and hardware cost reduces, but existing solutions lack interactive knowledge learning between different production sites
Solution Approach 1:
The cloud platform is designed with multi-functionality to serve both as a data storage repository and as an interactive learning environment. The same platform that stores process data from multiple production sites also enables cross-site knowledge transfer by allowing the reinforcement learning controllers to access and learn from aggregated data across different sites. This universal platform simultaneously provides data interaction convenience and enables adaptability through cross-site learning.
Solution Approach 2:
The system implements feedback loops where control outcomes from one production site are fed back into the cloud platform and used to update the reinforcement learning policies that are then applied at other sites. This feedback mechanism enables continuous improvement and cross-site knowledge learning, where successes and failures at one site inform control strategies at other sites, enhancing overall system adaptability.
4Ease of manufacture
If only local history data is used for ANN training, then training is simple to implement, but the controller cannot give proper control policy for situations never happened before
Solution Approach 1:
The system merges local historical data from each production site with data from other sites stored in the cloud platform. By combining these diverse data sources, the reinforcement learning controller is exposed to a broader range of operating conditions and scenarios that it might not encounter locally. This merged dataset enables the controller to learn more generalizable control policies that can handle novel situations while maintaining the relative simplicity of centralized training implementation.
Data Source
AI summary
A processor controller includes: a deep neutral network, for extracting, based upon feature information of process control data, from a process control data storage device, process control data available to a production device to be controlled, the feature information of the process control data including at least production device feature parameters and a production device load; and an enhanced neural network, for performing, based upon a process control prediction model, process control prediction by using real-time process control data of said production device. In an embodiment, the process control prediction model is trained by using the extracted available process control data. The process controller further includes a process control decision unit, for determining an operation control instruction for the production device based upon the result of process control prediction. As such, prediction accuracy and training efficiency of the process control prediction model of the process controller can be improved.