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A recurrent neural network approach for discrete-form stewart platform control with disturbance rejection

Year: 2025

Type: article

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.

Source: CAAI Artificial Intelligence Research

DOI: 10.26599/air.2025.9150047

Authors: Yang Shi, Yueyang Ma, Liangming Chen, Dimitrios K. Gerontitis, Long Jin

Institutions: Yangzhou University, Lanzhou University, International Hellenic University

Language: English

Topic: Robotic Mechanisms and Dynamics

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: diamond

Funders: National Natural Science Foundation of China, Bodossaki Foundation, Government of Jiangsu Province, Yangzhou University

OpenAlex ID: W4414974187