@Article{cmes.2022.020569,
AUTHOR = {Zhiguo Wang, Fangqing Gao, Fei Liu},
TITLE = {Improved High Order Model-Free Adaptive Iterative Learning Control with Disturbance Compensation and Enhanced Convergence},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {134},
YEAR = {2023},
NUMBER = {1},
PAGES = {343--355},
URL = {http://www.techscience.com/CMES/v134n1/49437},
ISSN = {1526-1506},
ABSTRACT = {In this paper, an improved high-order model-free adaptive iterative control (IHOMFAILC) method for a class of
nonlinear discrete-time systems is proposed based on the compact format dynamic linearization method. This
method adds the differential of tracking error in the criteria function to compensate for the effect of the random
disturbance. Meanwhile, a high-order estimation algorithm is used to estimate the value of pseudo partial derivative
(PPD), that is, the current value of PPD is updated by that of previous iterations. Thus the rapid convergence of the
maximum tracking error is not limited by the initial value of PPD. The convergence of the maximum tracking error
is deduced in detail. This method can track the desired output with enhanced convergence and improved tracking
performance. Two examples are used to verify the convergence and effectiveness of the proposed method.},
DOI = {10.32604/cmes.2022.020569}
}