@Article{JICS-10-106,
author = {Wang Teng},
title = {Robust Reinforcement Learning Decoupling Control Based on Int-egral Quadratic Constraints},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {10},
number = {2},
pages = {106--113},
abstract = {In order to keep stable in reinforcement learning process, a novel robust reinforcement learning 
decoupling  control  (RRLDC)  based  on  integral  quadratic  constraints（IQC）is  presented  in  this  paper.  It 
composes of a linear model to approximate the nonlinear plant, a state feedback K controller to generate  the 
basic control law, and an adaptive critic unit to evaluate decoupling performance, which tunes an actor unit to 
compensate decoupling action and model uncertainty as well as system nonlinearity. By replacing nonlinear 
and time-varying aspects  of a neural network   and  model  uncertainty  with  IQC, the stability of  the  control 
loop  is  analyzed.  As  a  result,  the  range  of  the  adjusted  parameters  is  found  within  which  the  stability  is 
guaranteed,  the  control  system  performance  is  improved  through  learning  and  the  algorithm  convergence 
speed  is accelerated. The proposed RRLDC  is applied to gas collector pressure control of coke  ovens. The 
simulation  results  show  the  proposed  control  strategy  can  not  only  obtain  the  good  performance,  but  also 
avoid  unstable behavior in learning process. It is an effective  multivariable decoupling control method for a 
class of strong coupling systems such as the gas collector pressure control of coke ovens.the effectiveness of 
proposed control strategy for the  collector gas pressure of coke ovens 
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22553.html}
}