Adaptive PID Tuning With Deep Reinforcement Learning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional PID controller tuning methods are limited by the need for manual intervention and assume a fixed process model, which can lead to suboptimal performance when process dynamics change, such as with sensor delays or valve stickiness, and require disturbing the process to estimate parameters.
Innovation Solution
A Deep Reinforcement Learning (DRL) agent is used to adaptively tune PID controllers by making incremental changes to tuning parameters based on rewards received from process states, allowing for continuous learning and improved control without requiring explicit model construction or manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional PID auto-tuning algorithms are used, then PID controller tuning can be automated, but the algorithms require disturbing the process and assume a fixed dynamic model which limits performance when process dynamics change
Solution Approach 1:
The patent applies dynamics by transitioning from static, fixed-model PID tuning to dynamic adaptive tuning using reinforcement learning. The RL agent continuously learns and adapts to changing process dynamics without requiring a predefined model, allowing the system to adjust to evolving process conditions such as sensor deadtime or valve stickiness automatically.
Solution Approach 2:
The patent changes the fundamental parameters of the tuning approach by moving from model-based parameter estimation to reinforcement learning-based parameter optimization. The RL agent learns optimal PID parameters through interaction with the process, changing how tuning parameters are determined from theoretical calculation to empirical learning.
2Manufacturing precision
If manual tuning of PID controllers is performed in large manufacturing facilities, then tuning can be customized, but it requires substantial resources and time to monitor and tune thousands of controllers
Solution Approach 1:
The patent applies self-service by enabling PID controllers to automatically tune themselves using reinforcement learning. Each controller or a central RL agent continuously optimizes tuning parameters without human intervention, allowing the system to self-adjust and maintain optimal performance across thousands of controllers without requiring manual tuning resources.
3Device complexity
If PID controllers use fixed tuning parameters, then the controller structure is simple, but performance deteriorates when process conditions change such as sensor deadtime or valve stickiness
Solution Approach 1:
The patent applies dynamics by making the previously static tuning parameters dynamic and adaptive. The reinforcement learning agent continuously adjusts PID parameters based on real-time process performance and changing conditions, transforming the controller from a fixed-structure system to an adaptive system that maintains reliability despite process variations.
Data Source
AI summary
Systems and methods are provided for using a Deep Reinforcement Learning (DRL) agent to provide adaptive tuning of process controllers, such as Proportional-Integral-Derivative (PID) controllers. The agent can monitor process controller performance, and if unsatisfactory, can attempt to improve it by making incremental changes to the tuning parameters for the process controller. The effect of a tuning change can then be observed by the agent and used to update the agent's process controller tuning policy. It has been unexpectedly discovered that providing adaptive tuning based on incremental changes in tuning parameters, as opposed to making changes independent of current values of the tuning parameters, can provide enhanced or improved control over a controlled variable of a process.