# Reinforcement Learning based Design of Linear Fixed Structure Controllers

arXiv: https://arxiv.org/abs/2005.04537 · HTML: https://mlsanity.com/paper/2005.04537/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 May 2020.

## 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)

Reinforcement learning has been successfully applied to the problem of tuning PID controllers in several applications. The existing methods often utilize function approximation, such as neural networks, to update the controller parameters at each time-step of the underlying process. In this work, we present a simple finite-difference approach, based on random search, to tuning linear fixed-structure controllers. For clarity and simplicity, we focus on PID controllers. Our algorithm operates on the entire closed-loop step response of the system and iteratively improves the PID gains towards a desired closed-loop response. This allows for embedding stability requirements into the reward function without any modeling procedures.