Lei Wang^1,2, Menghan Wei², Ziwei Huangfu³, Shunjie Zhu², Xuejian Ge^1,*, Zhengquan Li⁴

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-32, 2026, DOI:10.32604/cmc.2025.071295 - 09 December 2025

Abstract Iterative Learning Control (ILC) provides an effective framework for optimizing repetitive tasks, making it particularly suitable for high-precision applications in both precision manufacturing and intelligent transportation systems (ITS). This paper presents a systematic review of ILC’s developmental progress, current methodologies, and practical implementations across these two critical domains. The review first analyzes the key technical challenges encountered when integrating ILC into precision manufacturing workflows. Through case studies, it evaluates demonstrated improvements in positioning accuracy, surface finish quality, and production throughput. Furthermore, the study examines ILC’s applications in ITS, with particular focus on vehicular motion control More >

Zhiguo Wang^*, Fangqing Gao, Fei Liu

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 343-355, 2023, DOI:10.32604/cmes.2022.020569 - 24 August 2022

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 More >

Xiaoxin Yang¹, Saleem Riaz^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.2, pp. 605-626, 2022, DOI:10.32604/cmes.2022.020412 - 15 June 2022

Abstract An iterative learning control algorithm based on error backward association and control parameter correction has been proposed for a class of linear discrete time-invariant systems with repeated operation characteristics, parameter disturbance, and measurement noise taking PD type example. Firstly, the concrete form of the accelerated learning law is presented, based on the detailed description of how the control factor is obtained in the algorithm. Secondly, with the help of the vector method, the convergence of the algorithm for the strict mathematical proof, combined with the theory of spectral radius, sucient conditions for the convergence of More >