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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

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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.
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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
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-022-11027-w