Industrial Controller Action Sequencing With Offline RL Validation
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Solution Overview
Problem
Industrial control systems, particularly those using PID controllers, often require expert knowledge and time for calibration and lack efficient methods to optimize sequences of actions, making them suboptimal and potentially harmful when run sub-optimally.
Innovation Solution
A method and system utilizing reinforcement learning algorithms to determine an optimal sequence of actions for industrial controllers by simulating a validation environment using a predictive model, allowing for offline optimization based on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is applied to optimize controller sequences, then system performance is improved, but the complexity of calibration and validation increases
Solution Approach 1:
The patent creates a virtual copy of the industrial control system through a predictive model that simulates system behavior. This virtual environment allows reinforcement learning algorithms to be trained and validated without affecting the real system, eliminating the need for complex real-world calibration while maintaining optimization effectiveness.
Solution Approach 2:
The patent performs preliminary training and validation of reinforcement learning policies in a virtual environment before deploying them to the real system. By pre-optimizing controller sequences in simulation using historical data, the system avoids complex real-time calibration while ensuring safe and effective deployment.
2Productivity
If online testing of control sequences is performed, then model optimization is improved, but system safety and reliability deteriorate due to potential infrastructure damage
Solution Approach 1:
The patent uses a virtual copy (predictive model) of the industrial control system to test and validate control sequences. This allows unlimited online testing and optimization in the virtual environment without any risk to the real system, maintaining both optimization capability and system safety simultaneously.
Solution Approach 2:
The patent implements a safety buffer by validating all reinforcement learning policies in a virtual environment before real-world deployment. This preliminary validation acts as a cushion that prevents potentially harmful unvalidated sequences from being applied to the real system, ensuring reliability while enabling optimization.
3Measurement precision
If expert knowledge is used for PID controller calibration, then control accuracy is improved, but time consumption and cost increase
Solution Approach 1:
The patent enables the controller to self-optimize through reinforcement learning algorithms that automatically learn optimal control sequences from historical data. This eliminates the need for expert knowledge and manual calibration, achieving high control accuracy while dramatically reducing calibration time and costs.
Solution Approach 2:
The patent transforms the calibration process from manual expert adjustment to automated parameter optimization using reinforcement learning. By changing the approach from human expert tuning to algorithmic parameter optimization based on historical data, the system achieves accurate control without time-consuming manual calibration.
Data Source
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AI summary
A method for a controller in an industrial control system is described. The method comprises accessing a first subset of data in a dataset, the first subset comprising a plurality of tuples, each tuple comprising a first state of the industrial control system, an action associated to the controller interacting with the industrial control system, a second state of the industrial control 5 system, subsequent to the first state, that is transitioned into from the first state as a result of the controller performing the action and a parameter value in consideration of a parameter that is generated as a result of the industrial control system transitioning into the second state. The method further comprises evaluating a learning algorithm on the first subset, evaluating, in a validation environment, an action associated to the controller that is output by the learning10 algorithm and determining the sequence of actions to optimize the parameter on the basis of the evaluation.