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Volume 12 Issue 9
Sep.  2025

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  >  12(9): 1866-1877
G. Cheng, B. Qiu, J. Guo, and Y. Han, “A robust direct-discretized RNN for time-dependent optimization constrained by nonlinear equalities and its applications,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1866–1877, Sept. 2025. doi: 10.1109/JAS.2025.125627
Citation: G. Cheng, B. Qiu, J. Guo, and Y. Han, “A robust direct-discretized RNN for time-dependent optimization constrained by nonlinear equalities and its applications,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1866–1877, Sept. 2025. doi: 10.1109/JAS.2025.125627
shu

A Robust Direct-Discretized RNN for Time-Dependent Optimization Constrained by Nonlinear Equalities and Its Applications

doi: 10.1109/JAS.2025.125627
Funds:  This work was supported in part by the National Key Research and Development Program of China (2023YFC3011100), the National Natural Science Foundation of China (62476294), the Science and Technology Planning Project of Guangdong Province, China (2021B1212040017), and the Guangdong Basic and Applied Basic Research Foundation (2025A1515010377, 2023A1515110697)
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    Abstract
  • In recent years, numerous recurrent neural network (RNN) models have been reported for solving time-dependent nonlinear optimization problems. However, few existing RNN models simultaneously involve nonlinear equality constraints, direct discretization, and noise suppression. This limitation presents challenges when existing models are applied to practical engineering problems. Additionally, most current discrete-time RNN models are derived from continuous-time models, which may not perform well for solving essentially discrete problems. To handle these issues, a robust direct-discretized RNN (RDD-RNN) model is proposed to efficiently realize time-dependent optimization constrained by nonlinear equalities (TDOCNE) in the presence of various time-dependent noises. Theoretical analyses are provided to reveal that the proposed RDD-RNN model possesses excellent convergence and noise-suppressing capability. Furthermore, numerical experiments and manipulator control instances are conducted and analyzed to validate the superior robustness of the proposed RDD-RNN model under various time-dependent noises, particularly quadratic polynomial noise. Eventually, small target detection experiments further demonstrate the practicality of the RDD-RNN model in image processing applications.

     

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