Adaptive Fault-Tolerant Control for Unknown Affine Nonlinear Systems Based on Self-Organizing RBF Neural Network

• Published in: IEEE/CAA journal of automatica sinica Vol. 12; no. 9; pp. 1788 - 1800
• Main Authors: Chen, Ran, Zhou, Donghua, Sheng, Li
• Format: Journal Article
• Language: English
• Published: Piscataway Chinese Association of Automation (CAA) 01.09.2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuators; Adaptive control; Adaptive systems; Adaptive tracking control; Artificial neural networks; Closed loops; Control systems; Dependent variables; Fault tolerance; Fault tolerant systems; fault-tolerant control; Neural networks; Nonlinear systems; Observers; Radial basis function; Radial basis function networks; self-organizing radial basis function neural network (SRBFNN); Stability analysis; Tracking control; Tracking errors; Training; Uncertainty; unknown affine nonlinear system
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2025.125441

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.