Neuro-Sliding Control for Underwater ROV’s Subject to Unknown Disturbances

García-Valdovinos, Luis G.; Fonseca-Navarro, Fernando; Aizpuru-Zinkunegi, Joanes; Salgado-Jiménez, Tomás; Gómez-Espinosa, Alfonso; Cruz-Ledesma, José Antonio

doi:10.3390/s19132943

Cited by 24 publications

(17 citation statements)

References 15 publications

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“…Within them, machine learning algorithms are gaining quite an attention. They are being employed in different aspects such as navigation [150,151], obstacle avoidance and multi-AUVs formation control.…”

Section: Collaborative Auvs Overviewmentioning

confidence: 99%

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

et al. 2020

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Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

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Section: Collaborative Auvs Overviewmentioning

confidence: 99%

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

et al. 2020

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“…It will play with switching surface with bang-bang manner. This manner may produce jitter or chattering effect 16) . Table 1 shows the advantages and disadvantages of basic controller implemented.…”

Section: Figure 1: Rov Used In the Research Projectmentioning

confidence: 99%

A new tuning approach of Single Input Fuzzy Logic Controller (SIFLC) for Remotely Operated Vehicle (ROV) Depth Control

Zohedi¹,

Aras²,

Kasdirin³

et al. 2021

Evergreen

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Remotely Operated Vehicle (ROV) is an important vehicle to do underwater task. The uncertainty environment of underwater make it harder for ROV to maneuver and hold position at certain depth. Research on ROV controller for holding position had been conducted. Proportional, Integral and Derivative (PID), Fuzzy Logic Controller (FLC) and Single Input Fuzzy Logic Controller (SIFLC) was designed and compared. This paper discusses the modelling of developed ROV and tuning the SIFLC to get the best transient response. Steady state error (SSE), percent overshoot (%OS), time rise (Tr) and settling time (Ts) were analyzed to select the best controller.The result shows ROV depth can be controlled more precisely using SIFLC with 1.5 %OS, 11.5s Ts and 7.06s Tr.

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“…Owing to its distinguished learning capability and storage capacity, it is often utilised as a tool for estimation and identification [23]. In [24], a compound controller combining the backpropagation neural network and the second‐order SMC (SOSMC) scheme is proposed for under vehicles. Model‐free and chattering‐free are realised and the robustness of the system is improved.…”

Section: Introductionmentioning

confidence: 99%

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

Zhao

Wang

Huang

2020

IET Electric Power Applications

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In this study, an intelligent second‐order sliding mode control (SMC) method combining second‐order SMC (SOSMC) and recurrent radial basis function neural network (RRBFNN) applicable to the permanent magnet linear synchronous motor (PMLSM) is proposed to achieve high‐performance servo control fields. On the basis of a dynamic model of PMLSM and the SMC theory, the chattering problem in SMC is weakened and the tracking accuracy is improved by the design of SOSMC. As for the boundary of the uncertainty factors is difficult to obtain, the optimal performance of SOSMC is hard to achieve, the RRBFNN uncertainty observer is introduced for estimating the value of the uncertainty factors. Owing to the strong learning ability, the network parameters can be trained online. Besides, a robust compensator is developed to suppress the uncertainties such as approximation error, optimal parameter vector and higher Taylor series for further improving the robustness. Moreover, the adaptive learning algorithms are obtained by using the Lyapunov theorem to guarantee the asymptotical stability of the system. The experiments demonstrate that the proposed scheme provides high performance dynamic characteristics and strong robustness to uncertainties.

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Neuro-Sliding Control for Underwater ROV’s Subject to Unknown Disturbances

Cited by 24 publications

References 15 publications

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

A new tuning approach of Single Input Fuzzy Logic Controller (SIFLC) for Remotely Operated Vehicle (ROV) Depth Control

Intelligent second‐order sliding mode control for permanent magnet linear synchronous motor servo systems with robust compensator

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