2026-10-14T15:03:28Z https://www.scidb.cn/oai

10.57760/sciencedb.010du 2026-09-07T10:08:01Z

2026-09-07 Trajectory Tracking Simulation Dataset of RBF-ILC for Dual-Rotary Manipulator doi:10.57760/sciencedb.010du en To meet the demand for publicly available verification data of iterative learning control algorithms for EUV collector mirror inspection manipulators, this dataset provides simulation result data of trajectory tracking performance for the proposed RBF-enhanced adaptive iterative learning control (RBF-ILC) scheme. The dataset stores comparative curve data under PID control and RBF-ILC control for the dual-rotary manipulator subjected to time-varying inertia, nonlinear friction and periodic eccentric disturbances, which corresponds to the simulation conclusions in the associated journal paper. The released dataset contains comma-separated values (MAT) raw data files of tracking error curves over iterative cycles, as well as exported figure images of final comparative conclusion plots. The MAT files record sampling time, joint tracking errors under different control strategies, and iteration-dependent error decay data. Comparative results verify that the presented RBF-ILC achieves faster error convergence and more than 57 % reduction of steady-state residual error compared with conventional PID under coupled complex disturbances. This dataset can be reused for algorithm reproduction, comparative study of iterative learning control and neural-network-augmented robot control in repetitive ultra-precision inspection scenarios.  Simulation dataset; Trajectory tracking curves; Tracking error data XIN ZHOU EMBARGO https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank