An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles

Wang, Chao; Wang, Jing; Qin, Yichao; Rong, Shaowei

doi:10.3390/jmse12091473

Open AccessArticle

An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles

¹

School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China

²

Systems Engineering Research Institute, China State Shipbuilding Corporation, Beijing 100048, China

³

Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China

^*

Author to whom correspondence should be addressed.

J. Mar. Sci. Eng. 2024, 12(9), 1473; https://doi.org/10.3390/jmse12091473

Submission received: 20 June 2024 / Revised: 20 July 2024 / Accepted: 6 August 2024 / Published: 23 August 2024

(This article belongs to the Section Ocean Engineering)

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Abstract

This paper introduces an event-triggered sigmoid prescribed performance control method, enhanced by an adaptive characteristic model, for tracking the trajectory of autonomous underwater vehicles (AUVs). The AUV model is simplified into a function reliant solely on second-order parameter information through the use of characteristic modeling and a compression algorithm, which is then approximated by a neural network. We propose integrating prescribed performance control into event-triggered sliding mode control to accelerate convergence in AUV trajectory tracking. A novel prescribed performance function is employed in this integration, creating an event-triggered, non-singular terminal sliding mode control strategy. The stability of this controller is rigorously proven. This control strategy is not only robust against model uncertainties but also mitigates the jitter commonly associated with sliding mode control and the singularities from preset performance control due to sudden random disturbances. Comparative simulation experiments demonstrate that the proposed control method achieves superior control accuracy and a quicker response.

Keywords: AUV trajectory tracking; event-triggered control; prescribed performance control

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MDPI and ACS Style

Wang, C.; Wang, J.; Qin, Y.; Rong, S. An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles. J. Mar. Sci. Eng. 2024, 12, 1473. https://doi.org/10.3390/jmse12091473

AMA Style

Wang C, Wang J, Qin Y, Rong S. An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles. Journal of Marine Science and Engineering. 2024; 12(9):1473. https://doi.org/10.3390/jmse12091473

Chicago/Turabian Style

Wang, Chao, Jing Wang, Yichao Qin, and Shaowei Rong. 2024. "An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles" Journal of Marine Science and Engineering 12, no. 9: 1473. https://doi.org/10.3390/jmse12091473

APA Style

Wang, C., Wang, J., Qin, Y., & Rong, S. (2024). An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles. Journal of Marine Science and Engineering, 12(9), 1473. https://doi.org/10.3390/jmse12091473

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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An Adaptive Characteristic Model-Based Event-Triggered Sigmoid Prescribed Performance Control Approach for Tracking the Trajectory of Autonomous Underwater Vehicles

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