Reinforcement learning approach to autonomous PID tuning

Many industrial processes utilize proportional-integral-derivative (PID) controllers due to their practicality and often satisfactory performance. The proper controller parameters depend highly on the operational conditions and process uncertainties. This study combines the recent developments in computer sciences and control theory to address the tuning problem. It formulates the PID tuning problem as a reinforcement learning task with constraints. The proposed scheme identifies an initial approximate step-response model and lets the agent learn dynamics off-line from the model with minimal effort. After achieving a satisfactory training performance on the model, the agent is fine-tuned on-line on the actual process to adapt to the real dynamics, thereby minimizing the training time on the real process and avoiding unnecessary wear, which can be beneficial for industrial applications. This sample efficient method is tested and demonstrated through a pilot-scale multi-modal tank system. The performance of the method is verified through setpoint tracking and disturbance regulatory experiments. (c) 2022 Elsevier Ltd. All rights reserved.

Automated summary

PID controllers are widely used in industrial processes, but their parameter tuning is challenging. This study proposes an efficient method that formulates the PID tuning problem as a reinforcement learning task with constraints. It achieves satisfactory training performance on a model and then fine-tunes on the real process, minimizing training time and wear.