Production System Control With RL-Based PID Setpoint Tuning
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Solution Overview
Problem
Existing control methods for industrial systems with electric motors, such as PID control loops, are complex and difficult to generalize, requiring manual adjustments by trained personnel due to variations in motor types, environmental conditions, and machine-specific differences, leading to lengthy and costly setups that may not yield optimal results.
Innovation Solution
A method involving a training environment for a machine learning model simulating the production system, using reinforcement learning to train a model for specifying setpoints, integrating it into the system to automatically adjust PID controller parameters based on system-specific behaviors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual PID calibration is performed by trained personnel, then control precision can be adjusted for specific machine variations, but the setup process becomes lengthy and costly
Solution Approach 1:
The system performs preliminary actions by training the machine learning model offline using historical data and simulations before actual production. The model is pre-trained with various motor types and environmental conditions, so when deployed, it automatically adapts to the specific machine without requiring time-consuming manual calibration during setup
Solution Approach 2:
The machine learning model enables the control system to perform self-calibration automatically. The model receives real-time data from sensors and autonomously adjusts PID parameters without human intervention, replacing the need for trained personnel to manually tune controllers for each machine variation
2Measurement precision
If manual PID calibration is performed to account for machine variations, then control accuracy can be improved, but the process becomes complex and requires trained personnel
Solution Approach 1:
The patent replaces the mechanical/manual calibration process with an automated machine learning system. Instead of trained personnel manually adjusting parameters based on experience and trial-and-error, an AI model processes sensor data and automatically determines optimal PID parameters, simplifying the complex calibration process into an automated computational task
Solution Approach 2:
The system automatically changes control parameters based on machine-specific characteristics. The machine learning model analyzes variations in motor types, environmental conditions, and mechanical properties, then dynamically adjusts PID parameters to optimize control accuracy for each specific machine configuration without requiring complex manual procedures
3Ease of manufacture
If standard PID control loops are used for different machine configurations, then implementation is straightforward, but the system cannot adapt to variations in motor types and environmental conditions
Solution Approach 1:
The control system transitions from static, fixed PID parameters to dynamic, adaptive parameters. The machine learning model continuously monitors system behavior and automatically adjusts control parameters in real-time based on actual performance data, allowing the system to adapt to variations in motor types, environmental conditions, and mechanical wear while maintaining ease of implementation through automated adjustment
4Reliability
If manual adjustments are made for minor changes in machine components, then control performance can be optimized, but the process is costly and time-consuming
Solution Approach 1:
The machine learning model enables the control system to perform self-optimization automatically. When machine components vary or environmental conditions change, the model autonomously analyzes the new conditions and adjusts PID parameters to maintain optimal control performance, eliminating the need for costly and time-consuming manual re-calibration by trained personnel
Solution Approach 2:
The system implements continuous feedback loops where sensor data from the actual machine operation is fed back to the machine learning model. The model uses this real-time feedback to automatically detect performance deviations and adjust control parameters accordingly, ensuring optimal control performance adapts to minor changes in machine components without human intervention
Data Source
AI summary
A method for providing control of a production system is proposed, comprising the following steps: a.) providing a training environment for a machine learning model comprising a simulation of the production system to be controlled; b.) specifying a setpoint for a technical variable within the simulated production system and recording the resulting change in the technical variable over time as an output signal; c.) recording overshoot and/or rise time and/or dead time and/or settling time as control engineering metrics of the output signal; d.) training the model within the simulation using the method of reinforcement learning, wherein at least the setpoint selected in step b and the metrics of the output signal serve for training to provide a required Markov state; e.) Variation of the system behavior in the simulation for the next simulation epoch and re-execution of steps b, c, and d until the output signal metrics acquired from c for the simulated variants of the system behavior comply with system-specific limit values, wherein the model specifies a new setpoint in step b and the variation of the system behavior is based on the selection of predefined parameter sets from a database; and f.) Integration of the trained model into the production system, wherein the model assumes a control task for specifying at least one setpoint. The invention further relates to a device for controlling a production system.