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Neural Processing Letters
Erschienen in: Neural Processing Letters 4/2023

13.09.2022

Multi-dimensional Taylor Network-Based Fault-Tolerant Control for Nonlinear Systems with Unmodeled Dynamics and Actuator Faults

verfasst von: Arun Bali, Uday Pratap Singh, Rahul Kumar

Erschienen in: Neural Processing Letters | Ausgabe 4/2023

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Abstract

This work investigates the problem of Multi-dimensional Taylor Network (MTN)-based fault-tolerant control (FTC) for single-input and single-output nonlinear systems in non-strict feedback form. A MTN-based FTC method is presented for nonlinear systems with actuator faults and unmodeled dynamics. The actuator faults are contains both the loss of effectiveness factor of the actuator and a time-varying bias signal. MTN is used to approximate the unknown nonlinear functions, while unmodeled dynamics and dynamical disturbances are handled with the help of dynamical signal functions. A systemically backstepping-based fault-tolerant control scheme is proposed based on Lyapunov stability theory and MTN approximation ability. The suggested technique ensures that all closed-loop system signals are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small region around the origin. To demonstrate the effectiveness of the proposed controller design, three examples, including a single-link robot manipulator, are presented.
Vorheriger Artikel LMI-Based Synchronization Conditions to R-L Fractional Time-Varying Delayed Neural Networks with Parametric Uncertainty
Nächster Artikel Neural Combinatorial Optimization with Explanation

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Metadaten
Titel
Multi-dimensional Taylor Network-Based Fault-Tolerant Control for Nonlinear Systems with Unmodeled Dynamics and Actuator Faults
verfasst von
Arun Bali
Uday Pratap Singh
Rahul Kumar
Publikationsdatum
13.09.2022
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 4/2023
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11027-w

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