Open AccessAn adaptive PID controller with an online auto-tuning by a pretrained neural network
Author(s) -
P. A. Chertovskikh,
A. V. Seredkin,
O. A. Gobyzov,
A. S. Styuf,
M. G. Pashkevich,
M. P. Tokarev
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1359/1/012090
Subject(s) - pid controller , nonlinear autoregressive exogenous model , artificial neural network , control engineering , controller (irrigation) , autoregressive model , control theory (sociology) , computer science , matlab , nonlinear system , system identification , open loop controller , process (computing) , autoregressive–moving average model , engineering , artificial intelligence , temperature control , control (management) , data modeling , agronomy , physics , quantum mechanics , database , economics , econometrics , biology , operating system , closed loop
This paper describes an intelligent adaptive PID controller design procedure. The controller consists of a discrete time PID and an auto-tuning neural network unit. First system identification with a nonlinear autoregressive model (NARX) was performed. This model was then used to train the neural PID tuner. A special MATLAB toolbox “SmatPID Toolbox” was developed to automate the process of controller synthesis. The resulting controller was tested in a laboratory coal-gas furnace control system to track specified air flow rates.