Reinforcement Learning based Design of Linear Fixed Structure Controllers
Abstract
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.
Cite
@article{arxiv.2005.04537,
title = {Reinforcement Learning based Design of Linear Fixed Structure Controllers},
author = {Nathan P. Lawrence and Gregory E. Stewart and Philip D. Loewen and Michael G. Forbes and Johan U. Backstrom and R. Bhushan Gopaluni},
journal= {arXiv preprint arXiv:2005.04537},
year = {2021}
}
Comments
IFAC World Congress 2020