A Model-Based Reinforcement Learning Approach for Robust PID Tuning

Proportional-Integral-Derivative (PID) controller is widely used across various industrial process control applications because of its straightforward implementation. However, it can be challenging to fine-tune the PID parameters in practice to achieve robust performance. The paper proposes a model-based reinforcement learning (RL) framework to tune PID controllers leveraging the probabilistic inference for learning control (PILCO) method. In particular, an optimal policy given by PILCO is transformed into a set of robust PID tuning parameters for underactuated mechanical systems. The robustness of the devised controller is verified with simulation studies for a benchmark cart-pole system under server disturbances and system parameter uncertainties.

Automated summary

This paper proposes a framework that uses probabilistic inference for learning control (PILCO) to tune PID controllers, and applies it to underactuated mechanical systems. Simulation studies have verified the robust performance of the controller.