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Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach
In tests reported here, the meta-RL agent was trained entirely offline on first order plus time delay systems, and produced excellent results on novel systems drawn from the same distribution of process dynamics used for training.
Deep Reinforcement Learning with Shallow Controllers: An Experimental Application to PID Tuning
In addition to its simplicity, this approach has several appealing features: No additional hardware needs to be added to the control system, since a PID controller can easily be implemented through a standard programmable logic controller; the control law can easily be initialized in a "safe'' region of the parameter space; and the final product -- a well-tuned PID controller -- has a form that practitioners can reason about and deploy with confidence.
Optimal PID and Antiwindup Control Design as a Reinforcement Learning Problem
In this work, we focus on the interpretability of DRL control methods.
Reinforcement Learning based Design of Linear Fixed Structure Controllers
Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications.
