@article{Shi2025, 
author = {Yang Shi and Yueyang Ma and Liangming Chen and Dimitrios K. Gerontitis and Long Jin},
title = {A recurrent neural network approach for discrete-form stewart platform control with disturbance rejection},
year = {2025},
journal = {CAAI Artificial Intelligence Research},
volume = {4},
pages = {9150047},
keywords = {disturbance rejection, Stewart platform, recurrent neural networks (RNNs), discrete-form time-variant problem},
url = {https://www.sciopen.com/article/10.26599/AIR.2025.9150047},
doi = {10.26599/AIR.2025.9150047},
abstract = {Recurrent neural networks (RNNs) have been employed extensively as intelligent control approaches across various industrial control fields. However, existing research often lacks sufficient focus on discrete-form time-variant problems and disturbance rejection capability. This paper proposes a novel discrete-form integral-reinforcing RNN (DF-IR-RNN) approach. This approach integrates an innovative integral-reinforcing RNN (IR-RNN) design thought into the RNN approach to enhance the disturbance rejection capability in controlling the Stewart platform under discrete-form time-variant environment. Compared to traditional approaches, the proposed approach overcomes their limitations of disturbance rejection. The experimental results demonstrate that the proposed approach is highly effective in disturbance rejection and accurate trajectory tracking.}
}