Research Article
Feedback Feed-Forward Iterative Learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths
@INPROCEEDINGS{10.1007/978-3-031-73699-5_6, author={Sixian Xiong and Yun-Shan Wei and Mengtao Lei}, title={Feedback Feed-Forward Iterative Learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths}, proceedings={Security and Privacy in New Computing Environments. 6th International Conference, SPNCE 2023, Guangzhou, China, November 25--26, 2023, Proceedings}, proceedings_a={SPNCE}, year={2025}, month={1}, keywords={Iterative learning control iteratively varying trail lengths non-affine nonlinear system feedback control}, doi={10.1007/978-3-031-73699-5_6} }- Sixian Xiong
Yun-Shan Wei
Mengtao Lei
Year: 2025
Feedback Feed-Forward Iterative Learning Control for Non-affine Nonlinear Discrete-Time Systems with Varying Trail Lengths
SPNCE
Springer
DOI: 10.1007/978-3-031-73699-5_6
Abstract
This paper introduces a feedback and feedforward iterative learning control (ILC) scheme for non-affine nonlinear systems featuring iteratively varying trail lengths. The random trail lengths lead to the loss of tracking information in the final iteration. To address this information loss, the deviation in tracking for the ongoing iteration is incorporated with the aid of the feedback control component. It is demonstrated that the convergence condition is contingent solely on the feedforward control gain, with the feedback control part contributing to an acceleration in convergent speed. By establishing the statistical expectation of the initial state as equal to the desired state, it is proven that the mathematical expectation of the error can be effectively controlled to zero. The efficacy of the proposed algorithm is illustrated through numerical simulation.
