Smart PID generator for nonlinear systems: zero-shot reinforcement learning based on virtual environments

Area of Science:

• Control Engineering
• Artificial Intelligence
• Automation Systems

Background:

• Proportional-Integral-Derivative (PID) controllers are essential in industrial automation but challenging to tune for nonlinear systems.
• Existing auto-tuning methods, including reinforcement learning (RL), often require risky trial-and-error on live processes.

Purpose of the Study:

• To develop a safe and efficient offline training framework for PID parameter generation using virtual environments.
• To address performance variations in PID tuning across different operating conditions.

Main Methods:

• Utilized simplified first-order plus dead-time systems as virtual surrogate environments for RL agent training.
• Introduced a reward normalization mechanism to enhance reliability under varying conditions.
• Employed an enhanced actor-critic architecture for improved learning efficiency and policy convergence.

Main Results:

• The framework successfully generated optimal PID parameters for nonlinear systems without online retraining.
• Validated effectiveness and robustness through numerical simulations and industrial experiments.
• Demonstrated adaptive and reliable determination of PID parameters for complex systems.

Conclusions:

• The proposed PID generator framework effectively bridges advanced RL methodologies with practical industrial control.
• Enables safe and efficient offline tuning of PID controllers for nonlinear processes.
• Offers a viable solution for adaptive and robust industrial automation control.