Meta-RL Process Controller for Fast Industrial Tuning
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Solution Overview
Problem
Manual tuning of industrial process controllers is time-consuming and disrupts process quality, requiring skilled personnel and extensive online learning, which is not desirable in production environments.
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.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual tuning of process controller is performed, then the process controller can be adapted to process dynamics, but it requires significant time and skilled personnel
Solution Approach 1:
The system performs preliminary learning during simulated training phases before actual process operation. The meta-RL agent is pre-trained on a distribution of possible process dynamics, so when deployed to a real process, the adaptation is already largely complete, requiring minimal online learning time.
Solution Approach 2:
The process controller automatically tunes itself using the meta-RL algorithm without requiring skilled personnel intervention. The system self-adapts to process dynamics by collecting data during normal operation and automatically adjusting controller parameters, eliminating the need for manual expert tuning.
2Adaptability or versatility
If extensive online learning is performed for process controller tuning, then the controller adapts to the specific process, but it disturbs production and reduces quality or product yield
Solution Approach 1:
The meta-RL agent is trained offline on a distribution of simulated process dynamics before deployment. This preliminary training allows the controller to adapt to new processes with minimal online learning, avoiding disturbances to production and maintaining product quality during the adaptation phase.
Solution Approach 2:
The system uses feedback from the actual process during operation to fine-tune the controller parameters. The meta-RL agent continuously learns from process data while maintaining stable control, allowing adaptation without compromising quality through mechanisms like experience replay and gradual parameter updates.
3Ease of manufacture
If manual tuning process is used, then the process controller can be set up, but it requires skilled personnel and disrupts process quality
Solution Approach 1:
The process controller performs self-tuning using the meta-RL algorithm, automatically adapting to process dynamics without requiring skilled personnel. The system collects process data during normal operation and autonomously adjusts controller parameters, making the setup and maintenance processes automated and accessible to non-experts.
Solution Approach 2:
The patent replaces the manual mechanical tuning process with an automated computational system. The meta-RL algorithm substitutes human expertise and manual adjustment mechanisms with an automated learning system that processes data and determines optimal controller parameters through computational inference.
Data Source
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.