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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2025  > In Press, Accepted Manuscript
R. Chen, D. Zhou, and L. Sheng, “Adaptive fault-tolerant control for unknown affine nonlinear systems based on self-organizing RBF neural network,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–13, Sept. 2025.
Citation: R. Chen, D. Zhou, and L. Sheng, “Adaptive fault-tolerant control for unknown affine nonlinear systems based on self-organizing RBF neural network,” IEEE/CAA J. Autom. Sinica, vol. 12, no. 9, pp. 1–13, Sept. 2025.
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Adaptive Fault-Tolerant Control for Unknown Affine Nonlinear Systems Based on Self-Organizing RBF Neural Network

Funds:  This work was supported in part by the National Natural Science Foundation of China (62033008, 62188101, 62173343, 62073339)
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    Abstract
  • This article presents an adaptive fault-tolerant tracking control strategy for unknown affine nonlinear systems subject to actuator faults and external disturbances. To address the hyperparameter initialization challenges inherent in conventional neural network training, an improved self-organizing radial basis function neural network (SRBFNN) with an input-dependent variable structure is developed. Furthermore, a novel self-organizing RBFNN-based observer is introduced to estimate system states across all dimensions. Leveraging the reconstructed states, the proposed adaptive controller effectively compensates for all uncertainties, including estimation errors in the observer, ensuring accurate state tracking with reduced control effort. The uniform ultimate boundedness of all closed-loop signals and tracking errors is rigorously established via Lyapunov stability analysis. Finally, simulations on two different nonlinear systems comprehensively validate the effectiveness and superiority of the proposed control approach.

     

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