Adaptive Neural-Network-Based Fault-Tolerant Control for a Flexible String With Composite Disturbance Observer and Input Constraints

• Published in: IEEE transactions on cybernetics Vol. 52; no. 12; pp. 12843 - 12853
• Main Authors: Zhao, Zhijia, Ren, Yong, Mu, Chaoxu, Zou, Tao, Hong, Keum-Shik
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.12.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Actuator gain fault; Actuators; Adaptive control; adaptive fault-tolerant boundary control; Adaptive systems; Artificial neural networks; auxiliary system; Boundary control; Disturbance observers; Fault tolerance; Fault tolerant systems; flexible string (FS); Mathematical model; Neural networks; neural-network composite disturbance observer (NNCDO); Radial basis function; Stability analysis; Strings; Vibration analysis; Vibrations
• ISSN: 2168-2267; 2168-2275; 2168-2275
• DOI: 10.1109/TCYB.2021.3090417

We propose an adaptive neural-network-based fault-tolerant control scheme for a flexible string considering the input constraint, actuator gain fault, and external disturbances. First, we utilize a radial basis function neural network to compensate for the actuator gain fault. In addition, an observer is used to handle composite disturbances, including unknown approximation errors and boundary disturbances. Then, an auxiliary system eliminates the effect of the input constraint. By integrating the composite disturbance observer and auxiliary system, adaptive fault-tolerant boundary control is achieved for an uncertain flexible string. Under rigorous Lyapunov stability analysis, the vibration scope of the flexible string is guaranteed to remain within a small compact set. Numerical simulations verify the high control performance of the proposed control scheme.