Open AccessRobust iterative learning control for uncertain continuous‐time system with input delay and random iteration‐varying uncertainties
Author(s) -
ShokriGhaleh Hamid,
Ganjefar Soheil,
Shahri Alireza Mohammad
Publication year - 2021
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/cth2.12156
Subject(s) - iterative learning control , control theory (sociology) , monotonic function , tracking error , bounded function , mathematics , norm (philosophy) , convergence (economics) , computer science , trajectory , robust control , mathematical optimization , control system , control (management) , engineering , artificial intelligence , mathematical analysis , physics , electrical engineering , astronomy , political science , law , economics , economic growth
This study deals with the problem of robust iterative learning control (ILC) for linear continuous‐time systems with input delay subject to uncertainties in input delay, plant dynamic, reference trajectory, initial conditions and disturbances. Using the internal model control (IMC) structure in the frequency domain, an ILC scheme is proposed in which the IMC structure is responsible for coping with uncertainties in both delay time and plant dynamic. Sufficient conditions are derived to ensure that the tracking error expectation is bounded and converges monotonically to a small neighbourhood of zero (in the L 2 ‐norm sense) when uncertainties in reference trajectory, initial conditions and disturbances vary randomly from trial to trial. It is shown that the derived conditions are still valid to guarantee both boundedness and monotonic convergence of the tracking error variance (in the L 2 ‐norm sense). Illustrative examples are provided to demonstrate the effectiveness of the proposed method.