AI Control Function Updating for Adaptive PID Optimization
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Solution Overview
Problem
Conventional PID control systems rely on human experience and intuition for setting optimal parameters, which can be suboptimal and non-transferable across different environments and control systems, leading to performance deterioration due to environmental changes and operator replacements.
Innovation Solution
An automatic control artificial intelligence device that uses reinforcement learning to acquire output values, set base lines, and update control functions based on the gap between base lines and output values, allowing for adaptive optimization of control parameters.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If PID parameters are set by human experience and intuition, then the control system can be implemented with simple manual configuration, but the control performance deteriorates when environmental conditions change or operators are replaced
Solution Approach 1:
The control system automatically optimizes PID parameters through reinforcement learning without requiring manual intervention. The AI agent continuously learns from environmental feedback and self-adjusts control parameters, eliminating dependency on human operators while maintaining stable performance across changing conditions
Solution Approach 2:
The PID parameters are transformed from static manually-set values to dynamic values that automatically adapt to environmental changes. The reinforcement learning algorithm enables real-time parameter optimization based on current system state and performance feedback
2Adaptability or versatility
If reinforcement learning is used to automatically optimize control parameters, then control performance is improved and adaptability to environmental changes is enhanced, but the device complexity increases
Solution Approach 1:
The reinforcement learning framework is designed to be universally applicable across different control systems and environmental conditions. A single AI agent architecture can handle multiple control tasks by learning from diverse experiences, reducing the need for system-specific customization while maintaining high adaptability
Solution Approach 2:
The invention optimizes control by dynamically adjusting PID parameters through reinforcement learning. The AI agent learns optimal parameter values by exploring the parameter space and receiving feedback from system performance, enabling adaptive optimization without complex structural modifications
Data Source
AI summary
Disclosed herein is an automatic control artificial intelligence device including a collection unit configured to acquire an output value according to control of a control system; and an artificial intelligence unit operably coupled to the collection unit and configured to: communicate with the collection unit; set at least one of one or more base lines and a reward based on a gap between the one or more base lines and the output value, according to a plurality of operation goals of the control system; and update a control function for providing a control value to the control system by performing reinforcement learning based on the gap between the one or more base lines and the output value.