Industrial Controller Training With a Simulated Validation Environment
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Solution Overview
Problem
Industrial control systems, particularly those using PID controllers, often require expert knowledge and time for calibration and are suboptimal, and reinforcement learning methods face challenges in validating sequences of actions without a real-world environment.
Innovation Solution
A method and system that utilize a dataset to access tuples of system states, actions, and parameter values, applying a learning algorithm to determine an optimal sequence of actions through a validation environment, such as a predictive model, to optimize parameter performance without requiring real-world testing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If reinforcement learning is used to optimize controller sequences, then productivity and performance are improved, but the ability to validate and test the model in real-world environments is limited or impossible
Solution Approach 1:
The patent creates a virtual copy of the industrial control system environment where reinforcement learning models can be trained and validated. This virtual environment replicates the physical system's behavior, allowing extensive testing and validation without risking actual system operation. The virtual environment serves as a safe sandbox for experimenting with different controller sequences and evaluating their performance before deployment.
2Reliability
If PID controllers are used in industrial control systems, then reliability is maintained, but expert knowledge and time are required for calibration and they remain suboptimal
Solution Approach 1:
The reinforcement learning controller performs self-calibration and self-optimization by learning from interactions with the virtual environment. Instead of requiring expert calibration, the system automatically adjusts its parameters and sequences through the reinforcement learning process, using rewards and penalties to guide optimization. This eliminates the need for manual expert intervention while achieving superior performance compared to traditional PID controllers.
3Ease of operation
If traditional control methods are used, then ease of operation is maintained, but manufacturing precision and performance optimization are limited
Solution Approach 1:
The reinforcement learning model performs preliminary optimization in the virtual environment before actual deployment. All complex learning, training, and sequence optimization occur beforehand in the simulated setting. When deployed to the real system, the pre-optimized controller sequences are executed directly, maintaining ease of operation while achieving high manufacturing precision through the advanced optimization performed in advance.
Data Source
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 with the controller interacting with the industrial control system, a second state of the industrial control 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.