A Variable-Order Fuzzy Logic Controller Design Method for an Unmanned Underwater Vehicle Based on NSGA-II

Zhu, Boyu; Liu, Lu; Zhang, Lichuan; Liu, Mingwei; Duanmu, Yixiang; Chen, Yi; Dang, Peidong; Li, Jiabao

doi:10.3390/fractalfract6100577

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…A controller design method for UUV depth control (VD-SIFLC) based on fractional calculus, fuzzy control, dynamic parameters, and a fast non-dominated sorting genetic algorithm (NSGA-II) has been proposed in [119]. Simulation results show that the controlled system with the VD-FIFLC could achieve better robustness and dynamic and steadystate performance…”

Section: B the Latest Control Systemsmentioning

confidence: 99%

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

Hasan,

Ahmad,

Liaf

et al. 2024

IEEE Access

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Autonomous Underwater Vehicles (AUVs) epitomize a revolutionary stride in underwater exploration, seamlessly assuming tasks once exclusive to manned vehicles. Their collaborative prowess within joint missions has inaugurated a new epoch of intricate applications in underwater domains. This study's primary aim is to scrutinize recent technological advancements in AUVs and their role in navigating the complexities of underwater environments. Through a meticulous review of literature and empirical studies, this review synthesizes recent technological strides, spotlighting developments in biomimicry models, cuttingedge control systems, adaptive navigation algorithms, and pivotal sensor arrays crucial for exploring and mapping the ocean floor. The article meticulously delineates the profound impact of AUVs on underwater robotics, offering a comprehensive panorama of advancements and illustrating their far-reaching implications for underwater exploration and mapping. This review furnishes a holistic comprehension of the current landscape of AUV technology. This condensed overview furnishes a swift comparative analysis, aiding in discerning the focal points of each study while spotlighting gaps and intersections within the existing body of knowledge. It efficiently steers researchers toward complementary sources, enabling a focused examination and judicious allocation of time to the most pertinent studies. Furthermore, it functions as a blueprint for comprehensive studies within the AUV domain, pinpointing areas where amalgamating multiple sources would yield a more comprehensive understanding. By elucidating the purpose, employing a robust methodology, and anticipating comprehensive results, this study endeavors to serve as a cornerstone resource that not only encapsulates recent technological strides but also provides actionable insights and directions for advancing the field of underwater robotics.

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Section: B the Latest Control Systemsmentioning

confidence: 99%

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

Hasan,

Ahmad,

Liaf

et al. 2024

IEEE Access

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“…Du et al [77] also presented a similar control algorithm for the control system design of UUV. The block diagram of the fuzzy controller is shown in Figure 8 [78]. Fuzzy logic was introduced for the first time in 1965, and afterward, its use for control systems increased quickly.…”

Section: Fuzzy Control Algorithmmentioning

confidence: 99%

“…Du et al [77] also presented a similar control algorithm for the control system design of UUV. The block diagram of the fuzzy controller is shown in Figure 8 [78]. Membership functions were used by Boyu et al [78] to define the inputs.…”

Section: Fuzzy Control Algorithmmentioning

confidence: 99%

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A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles

Bashir,

Khan,

Iqbal

et al. 2023

Sustainability

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Unmanned underwater vehicles (UUVs) have become increasingly popular in recent years due to their use in various applications. The motivations for using UUVs include the exploration of difficult and dangerous underwater environments, military tasks in mine detection, intelligence gathering and surveillance, the inspection of offshore oil and gas infrastructure in the oil and gas industry, scientific research for studying marine life, and the search and rescue of missing persons or submerged airplanes or boats in underwater environments. UUVs offer many advantages in achieving the desired applications with increased safety, efficiency, and cost-effectiveness. However, there are also several challenges associated with their communication, navigation, power requirements, maintenance, and payload limitations. These types of vehicles are also prone to various disturbances caused by currents of the ocean, propulsion systems, and unmolded uncertainties. Practically, it is a challenging task to design a controller that will ensure optimal performance under these conditions. Therefore, the control system design is of prime importance in the overall development of UUVs. Also, the UUV controller receives input from different sensors, and the data from these sensors are used by the controller to perform different tasks. The control systems of UUVs should take into account all uncertainties and make them stable so that all sensors can perform optimally. This paper presents a complete review of different control system design algorithms for UUVs. The basic logic designs of several control system algorithms are also presented. A comparison is made based on reliability, robustness, precession, and the ability of the controller to handle the nonlinearity that is faced by UUVs during their missions. Simulation and experimental results are thoroughly studied to gain insight into each algorithm. The advantages and disadvantages of each algorithm are also presented, which will facilitate the selection of a suitable algorithm for the control system design of UUVs.

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“…Several approaches have been identified from the literature for AUV local path planning. They include rapidly-exploring random trees (RRT) [9], [10], fuzzy logic algorithms [11], [12], and machine learning (ML) techniques such as supervised and reinforcement learning [13]. Fig.…”

Section: Introductionmentioning

confidence: 99%

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

et al. 2023

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Autonomous underwater vehicles (AUVs) have become attractive and essential for underwater search and exploration because of the advantages they offer over manned underwater vehicles. Hence the need to improve AUV technologies. One crucial area of AUV technology involves efficiently solving the path planning problem. Several approaches have been identified from the literature for AUV global and local path planning. The use of machine learning (ML) techniques in overcoming some of the challenges associated with AUV path planning problems such as safety and obstacle avoidance, energy consumption, and optimal time and distance travelled remains an active research area. While there is literature on global and local path planning that explores different techniques, there is still a lack of paper that provides an overview of the application of ML for local path planning. Hence the main objective of this paper is to present an overview of the state-of-the-art application of ML techniques on local path planning for AUVs. The ML algorithms are discussed under supervised, unsupervised, and reinforcement learning. The challenges faced in real-life deployment, simulated scenarios, computational issues, and application of ML algorithms are discussed, with future research directions presented.INDEX TERMS Machine learning, local path planning, autonomous underwater vehicle (AUV), real-time path planning, underwater.

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A Variable-Order Fuzzy Logic Controller Design Method for an Unmanned Underwater Vehicle Based on NSGA-II

Cited by 6 publications

References 25 publications

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

Oceanic Challenges to Technological Solutions: A Review of Autonomous Underwater Vehicle Path Technologies in Biomimicry, Control, Navigation, and Sensing

A Review of the Various Control Algorithms for Trajectory Control of Unmanned Underwater Vehicles

An Overview of Machine Learning Techniques in Local Path Planning for Autonomous Underwater Vehicles

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