Meta-RL Process Controller Tuning for New Industrial Processes
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Solution Overview
Problem
The manual process of determining process dynamics and tuning industrial process controllers is time-consuming and disrupts product quality, requiring skilled personnel and extensive online learning, which is not desirable in industrial settings.
Innovation Solution
A meta-reinforcement learning (MRL) system using a deep reinforcement learning algorithm and an embedding neural network to generate a multidimensional vector representing process dynamics and control objectives, allowing the process controller to adapt to new industrial processes with minimal online learning, thereby automating the tuning of process controllers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning of process controllers is performed, then the controller can be adapted to process dynamics, but the process requires significant time and skilled personnel, and disrupts product quality or yield
Solution Approach 1:
The system performs preliminary learning of process dynamics offline before actual control is needed. The neural network is trained in advance on historical process data, so when tuning is required, the controller can quickly adapt without time-consuming manual intervention. This resolves the contradiction by preparing the adaptation capability beforehand.
Solution Approach 2:
The process controller automatically tunes itself using the neural network to identify process dynamics and optimize control parameters without requiring skilled personnel. The system serves its own tuning needs by leveraging online process data and the pre-trained neural network model, eliminating dependence on expert operators.
2Adaptability or versatility
If extensive online learning is performed for process controller tuning, then the controller can adapt to new processes, but production is disturbed and quality or product yield is reduced
Solution Approach 1:
The neural network is pre-trained offline on extensive process data before deployment. This preliminary learning phase captures the essential dynamics and relationships without interfering with production. When the controller needs to adapt to new processes, it uses this pre-learned knowledge rather than performing extensive online learning, thus maintaining product quality while achieving adaptability.
Solution Approach 2:
The system creates a virtual model or copy of the process dynamics through the neural network, trained on historical data. This digital twin or model allows the controller to learn and adapt to new processes by simulating and analyzing the copied process behavior rather than requiring extensive real-world experimentation that would disturb production and affect quality.
3Measurement precision
If manual process dynamics determination is performed, then accurate process understanding is achieved, but the process requires skilled personnel and significant time investment
Solution Approach 1:
The system replaces manual expert analysis with an automated neural network-based approach. Instead of relying on skilled personnel to manually determine process dynamics through experience and analysis, the neural network automatically processes process data to identify dynamics and characteristics, achieving accurate process understanding without human intervention.
Solution Approach 2:
The controller automatically determines process dynamics using the neural network and online process data without requiring skilled personnel. The system performs self-diagnosis and self-characterization by analyzing process behavior patterns, making the complex task of process dynamics determination easy to operate while maintaining high measurement precision.
Data Source
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AI summary
A method includes providing a data processing system that stores a deep reinforcement-learning algorithm (DRL). The data processing system is configured to train the DRL. The data processing system will also include the latent vector that adapts a process controller to a new industrial process. The data processing system will also train a meta-RL agent using a meta-RL training algorithm. The meta-RL training algorithm trains the meta-RL agent to find a suitable latent state to control the new process.