Fuzzy Controller Design for Autonomous Underwater Vehicles Path Tracking

Nguyễn, Duy Anh; Thanh, Do Duy; Tien, Nguyen Tran; Anh, Pham Viet

doi:10.1109/icsse.2019.8823333

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…Another improved method with dynamic SMC was proposed to improve robustness overactuated autonomous underwater when exposed to under ocean current and model uncertainties [12,13]. Other strategies, such as fuzzy logic (FL) [14] and the model predictive controller (MPC) [15], have produced noteworthy outcomes. In [14], FL is integrated with the Kalman filter to assess the position of AUVs and to drive them using a specified route.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Unmanned Underwater Vehicles with Online Disturbance Compensation Scheme

Azid,

Mehta,

Kuar

et al. 2024

Journal of Robotics

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With the advancement in robotics technology over the recent years, underwater robots’ design and development are gaining interest. Unmanned underwater vehicles (UUVs) have many applications in aquaculture, deep-sea exploration, research, and enhanced rescue tasks. However, various factors must be considered when developing any underwater vehicle system to explore the deep ends of the underwater world. In this paper, we develop the most suitable model for understanding various system parameters. The new mathematical model considers certain constraints and external disturbances exerted on the system. Also, a control strategy is suggested for the UUV’s stability and robustness. The suggested observer and model are simple, allowing for accurate estimations of all system states and the global impacts of unknown limped perturbations with a minimal computational cost.

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Section: Introductionmentioning

confidence: 99%

Dynamic Modeling of Unmanned Underwater Vehicles with Online Disturbance Compensation Scheme

Azid,

Mehta,

Kuar

et al. 2024

Journal of Robotics

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show abstract

“…e most important part of a fuzzy controller is the fuzzy control law [22][23][24]. e fuzzy control law is the control experience summarized by researchers through the operation, learning, and accumulation of the controlled object system.…”

Section: Introductionmentioning

confidence: 99%

An Embedded Linear Model Three-Dimensional Fuzzy PID Control System for a Bionic AUV under Wave Disturbance

Lyu

et al. 2022

Mathematical Problems in Engineering

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This study provides a new embedded linear model (ELM) three-dimensional fuzzy PID control method for a bionic autonomous underwater vehicle (AUV), which is disturbed by ocean waves. The uncertainty of the ocean waves will make it difficult for the AUV to achieve good sailing quality, especially near the sea surface. In view of this problem, an ELM fuzzy PID controller, which adopts three-dimensional fuzzy control rules, is designed. In the ELM fuzzy PID controller, a linear model of AUV is established in order to actively perceive the effect of waves on AUV. Hence, the wave disturbance can be reflected in the control signal earlier and faster. Finally, three simulations are carried out to verify the control effect of the ELM fuzzy PID controller. An ocean wave disturbance model is established in order to simulate the force and the moment acting on the AUV. The results imply that compared with the classical PID controller and normal fuzzy PID controller, the ELM fuzzy PID controller can significantly enhance the stability of AUV and make the response faster and overshoot smaller.

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Path Tracking Control of Hybrid-driven Robotic Fish Based on Deep Reinforcement Learning

Yue

Zhang

2020

2020 IEEE International Conference on Mechatronics and Automation (ICMA)

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Fuzzy Controller Design for Autonomous Underwater Vehicles Path Tracking

Cited by 6 publications

References 4 publications

Dynamic Modeling of Unmanned Underwater Vehicles with Online Disturbance Compensation Scheme

Dynamic Modeling of Unmanned Underwater Vehicles with Online Disturbance Compensation Scheme

An Embedded Linear Model Three-Dimensional Fuzzy PID Control System for a Bionic AUV under Wave Disturbance

Path Tracking Control of Hybrid-driven Robotic Fish Based on Deep Reinforcement Learning

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