Open AccessData‐driven optimal terminal iterative learning control with initial value dynamic compensation
Author(s) -
Chi Ronghu,
Huang Biao,
Wang Danwei,
Zhang Ruikun,
Feng Yuanjing
Publication year - 2016
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2015.0824
Subject(s) - iterative learning control , control theory (sociology) , convergence (economics) , optimal control , process (computing) , initial value problem , terminal (telecommunication) , compensation (psychology) , computer science , mathematical optimization , batch processing , iterative method , control (management) , mathematics , artificial intelligence , psychology , telecommunications , psychoanalysis , mathematical analysis , economics , programming language , economic growth , operating system
Iterative learning control is an effective control strategy for control of batch processes and initial condition is one of the most important factors affecting convergence of iterative learning batch process control. In this study, a novel initial value dynamic compensation‐based data‐driven optimal terminal iterative learning control (IDC‐DDOTILC) approach is proposed for non‐linear systems under random initial conditions. The unknown influence on the terminal output caused by the initial states is deduced by using a dynamical linearisation of the controlled non‐linear system along the iteration direction, and then the unknown influence is estimated iteratively and incorporated into the learning control law. As a result, the proposed IDC‐DDOTILC can drive the terminal output of the plant to attain the target value at the endpoint asymptotically under iteration‐varying initial conditions. Two chemical engineering examples including a batch reactor and a fed‐batch ethanol fermentation process are used to demonstrate effectiveness of the proposed control algorithm.