Open AccessParameter optimised iterative learning control algorithm for multi‐batch reactor
Author(s) -
Gao Shida,
Li Jun,
Bo Cuimei,
Yin Junhua,
Liu Yanping
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2019.1070
Subject(s) - iterative learning control , tracking error , convergence (economics) , computer science , tracking (education) , markov chain , control theory (sociology) , process (computing) , pid controller , markov process , trajectory , batch processing , algorithm , control (management) , mathematical optimization , mathematics , control engineering , artificial intelligence , engineering , machine learning , temperature control , psychology , pedagogy , statistics , physics , astronomy , economics , programming language , economic growth , operating system
A two‐dimensional iterative learning PID control algorithm with Markov tuning method for batch reaction process is presented in this study. The learning algorithm with parameters tuned by Markov method can be explicitly tackling the repetitiveness of batch process, while achieve precise tracking of preset trajectory. It further shows that the control algorithm can guarantee convergence of the tracking error. Simulation results show that the parameters tuned by the Markov method can make the tracking error converge faster compared with the intimal control method.