Iterative learning control for a class of non-affine nonlinear systems with input saturation and external disturbances

For a class of nonlinear non-affine systems subjected to external environmental disturbances and input saturation, the iterative learning control method is applied to design a controller that can achieve convergence of system error within a finite number of iterations at the required precision. First, we perform anti-saturation transformation on the system input state with constraints. Then, we use fuzzy function approximation to deal with the external environmental disturbances. Next, we construct the iterative error and prove that under the designed controller, the system error can converge after a finite number of iteration. Finally, we verify the effectiveness of the proposed method using a simulation platform.

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Publication Details

Journal
Transactions of the Institute of Measurement and Control
Published
2026-05-21
DOI
https://doi.org/10.1177/01423312261441654
Primary Topic
Iterative Learning Control Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Iterative learning control for a class of non-affine nonlinear systems with input saturation and external disturbances

Qi Cheng, H Chen, Y W Xu, Peng Zhaoqing
Transactions of the Institute of Measurement and Control
Iterative Learning Control Systems
article

Iterative learning control for a class of non-affine nonlinear systems with input saturation and external disturbances

Qi Cheng, H Chen, Y W Xu, Peng Zhaoqing
article en

Abstract

For a class of nonlinear non-affine systems subjected to external environmental disturbances and input saturation, the iterative learning control method is applied to design a controller that can achieve convergence of system error within a finite number of iterations at the required precision. First, we perform anti-saturation transformation on the system input state with constraints. Then, we use fuzzy function approximation to deal with the external environmental disturbances. Next, we construct the iterative error and prove that under the designed controller, the system error can converge after a finite number of iteration. Finally, we verify the effectiveness of the proposed method using a simulation platform.

Transactions of the Institute of Measurement and Control
Nanjing Tech University (CN), Hohai University (CN)
Openalex Percentile: Top 7%
Iterative Learning Control Systems
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