# A Model-Based Reinforcement Learning Approach for PID Design

arXiv: https://arxiv.org/abs/2206.03567 · HTML: https://mlsanity.com/paper/2206.03567/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2022.

## Built on by (most cited first, 1 of 1)

- [PID-Inspired Inductive Biases for Deep Reinforcement Learning in Partially Observable Control Tasks](https://mlsanity.com/paper/2307.05891/) (2023)

## Abstract (arXiv)

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 design PID controllers leveraging the probabilistic inference for learning control (PILCO) method and Kullback-Leibler divergence (KLD). Since PID controllers have a much more interpretable control structure than a network basis function, an optimal policy given by PILCO is transformed into a set of robust PID tuning parameters for underactuated mechanical systems. The presented method is general and can blend with several model-based and model-free algorithms. The performance of the devised PID controllers is demonstrated with simulation studies for a benchmark cart-pole system under disturbances and system parameter uncertainties.