Event‐Driven Fixed‐Time Cooperative Attitude Control of High‐Speed Unmanned System Considering Input Saturation and Actuator Failure

ABSTRACT The event‐driven fixed‐time cooperative attitude controller for high‐speed unmanned system (HSUS) with input saturation and actuator failure is constructed in this article. Considering the limited system resources, the event‐triggered mechanism (ETM) is employed to reduce the frequency of controller updates. To better align with the actual situation, both input saturation and actuator failure are taken into account when designing the system controller. Subsequently, the radial basis function neural networks (RBF NNs) are implemented for efficiently approximating and handling unknown nonlinear relationships within the system. Furthermore, the fixed‐time convergence theorem is implemented to achieve improved transient performance. Ultimately, the effectiveness of the proposed control strategy is confirmed through a series of simulation examples.

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Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2026-01-19
DOI
https://doi.org/10.1002/rnc.70393
Primary Topic
Adaptive Dynamic Programming Control
Type
article
Field-Weighted Citation Impact
0.00
Controls
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article

Event‐Driven Fixed‐Time Cooperative Attitude Control of High‐Speed Unmanned System Considering Input Saturation and Actuator Failure

Caisheng Wei, Xuyang Wang, Yi Wang, Xin Ning et al.
International Journal of Robust and Nonlinear Control
Adaptive Dynamic Programming Control
article

Event‐Driven Fixed‐Time Cooperative Attitude Control of High‐Speed Unmanned System Considering Input Saturation and Actuator Failure

Caisheng Wei, Xuyang Wang, Yi Wang, Xin Ning, Zheng Wang
article en

Abstract

ABSTRACT The event‐driven fixed‐time cooperative attitude controller for high‐speed unmanned system (HSUS) with input saturation and actuator failure is constructed in this article. Considering the limited system resources, the event‐triggered mechanism (ETM) is employed to reduce the frequency of controller updates. To better align with the actual situation, both input saturation and actuator failure are taken into account when designing the system controller. Subsequently, the radial basis function neural networks (RBF NNs) are implemented for efficiently approximating and handling unknown nonlinear relationships within the system. Furthermore, the fixed‐time convergence theorem is implemented to achieve improved transient performance. Ultimately, the effectiveness of the proposed control strategy is confirmed through a series of simulation examples.

International Journal of Robust and Nonlinear Control
Central South University (CN), Northwestern Polytechnical University (CN), Institute of Electronics (CN), Northwest Institute of Mechanical and Electrical Engineering (CN)
Openalex Percentile: Top 82%
Adaptive Dynamic Programming Control
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