YU-HSIU LEECheng, Yi-TaiYi-TaiChengYuan, Kai-ShiangKai-ShiangYuanTsao, Tsu-ChinTsu-ChinTsao2025-07-312025-07-312025-0909473580https://www.scopus.com/record/display.uri?eid=2-s2.0-105009621646&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/730837This paper presents a novel data-driven iterative learning control (ILC) algorithm for stabilized multivariable nonlinear dynamical systems. The proposed algorithm incorporates two data-driven learning mechanisms: an adaptive feedforward algorithm that models perturbed dynamics as an unknown linear time-varying system and minimizes RMS errors with respect to an LTI reference model, followed by a second learning mechanism for fast convergence in trajectory tracking. For multivariate systems, the non-commutative nature of the cascade of systems necessitates the use of the right inverse for adaptive model matching to promote error convergence. To address challenges in adaptive filtering, a transposition-based technique is introduced to obtain the right inverse for square and over-actuated systems. For ni-input-no-output systems, the approach necessitates conducting ni×no experiments. An efficient algorithm is proposed to reduce this requirement by reorganizing impulse response matrix components. The effectiveness of the proposed methods is demonstrated through both simulations and experiments.falseAdaptive model matchingDynamic couplingIterative learning controlMultivariate inversionjournal article10.1016/j.ejcon.2025.1012732-s2.0-105009621646