Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization

Chen, JiaWang; Zhu, Huangchao; Zhang, Lei; Sun, Yingying

doi:10.1016/j.oceaneng.2018.03.010

Cited by 78 publications

(29 citation statements)

References 8 publications

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“…GA, PSO, SVM, ACO, FFOA, ABC, and FA algorithms are often used for ship model parameters identification [32,[195][196][197][198][199][200][201] or ship controller parameter adjustment. For example, optimizing ship fuzzy controller by introducing GA and PSO algorithm [108,202,203]; Optimizing the tuning parameters of ship PID controller by introducing GA and ACO [50,66]; Optimizing the course controller parameters based on SVM by introducing PSO [199]; Or optimizing the parameters of controller based on backstepping control and disturbance observer by FA [69].…”

Section: Number Algorithm Number Algorithmmentioning

confidence: 99%

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Wang

Liu

et al. 2019

JMSE

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At present, with the development of waterborne transport vehicles, research on ship faces a new round of challenges in terms of intelligence and autonomy. The concept of maritime autonomous surface ships (MASS) has been put forward by the International Maritime Organization in 2017, in which MASS become the new focus of the waterborne transportation industry. This paper elaborates on the state-of-the-art research on motion control of MASS. Firstly, the characteristics and current research status of unmanned surface vessels in MASS and conventional ships are summarized, and the system composition of MASS is analyzed. In order to better realize the self-adaptability of the MASS motion control, the theory and algorithm of ship motion control-related systems are emphatically analyzed under the condition of classifying ship motion control. Especially, the application of intelligent algorithms in the ship control field is summarized and analyzed. Finally, this paper summarizes the challenges faced by MASS in the model establishment, motion control algorithms, and real ship experiments, and proposes the composition of MASS motion control system based on variable autonomous control strategy. Future researches on the accuracy and diversity of developments and applications to MASS motion control are suggested.

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Section: Number Algorithm Number Algorithmmentioning

confidence: 99%

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Wang

Liu

et al. 2019

JMSE

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“…Path planning algorithm based on a searching strategy from Darwin's theories of evolution was introduced in (Goldberg and Holland, 1988). The Genetic Algorithm (GA) (Kim et al, 2017;Chen et al, 2018;Fu et al, 2018) and its inheritance are the most popular examples in this area, where a specific number of candidate paths are maintained and these paths are iteratively evolved and selected by applying genetic operators. GAs have also been applied in the AUV path planning problem (Alvarez et al, 2004;Zadeh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Niu

Chai

et al. 2020

Ocean Engineering

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Unmanned Surface Vehicles (USVs) are increasingly used for ocean missions, which typically require long duration of operations under strict energy constraints. Consequently, there is an increased interest in energy efficient path planning for USVs. This work proposes a novel energy efficient path planning algorithm to address the challenges with the presence of spatially-temporally variant sea current and complex geographic map data, by integrating the following algorithms, namely Voronoi roadmap, Dijkstras searching, coastline expanding and genetic algorithm. The selection, crossover and mutation operators are employed as part of the GA algorithm. The dividing, smoothing and exchanging operators are proposed to improve the quality of the path and adapt to the Voronoi-Visibility roadmap. The Global Self-Consistent Hierarchical High-Resolution Shorelines dataset and historical sea current dataset are applied to demonstrate the flexibility and practicability of the proposed algorithm. To evaluate the performance, the Voronoi-GA energy efficient algorithm and Voronoi-Visibility energy efficient path re-planning algorithm are also im

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“…Lazarowska et al created a novel ant colony optimization method for trajectory planning in the complex environment, especially in a collision situation on the sea [23][24][25]. Fuzzy control and genetic algorithm are combined to establish a route planning system for underwater vehicles, which could provide strong robustness [26]. The route planning algorithm is one of the aspects of obstacle avoidance system for unmanned surface vehicle.…”

Section: Introductionmentioning

confidence: 99%

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

et al. 2020

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In order to complete automatic route planning in the complex navigation environment, this paper proposes a quadratic optimization genetic algorithm combining the motion characteristics of the ships. Furthermore, the constraints of the maneuvering characteristics of ship are considered to calculate the accurate planning routes fast. First of all, the turning and speed reduction model of ships are established, which is the foundation for the accurate calculation of approach states between the own ship and target ships. Second, in order to check the position relationship between the planned route and the land objects (such as the islands and rocks) on the chart, a detection algorithm between lines and irregular polygon boundary based on quadtree method is acquired. Third, a new genetic algorithm based on double-cycling optimization is proposed by this paper. Aiming at the complex problem of comprehensive collision avoidance planning under the conditions of static and dynamic obstacles, this paper realizes an efficient and feasible solution of dynamic programming for ship route. Finally, the algorithm is verified on a test fireboat, and the experiment data prove the feasibility and effectiveness of the proposed algorithm by this paper.

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Research on fuzzy control of path tracking for underwater vehicle based on genetic algorithm optimization

Cited by 78 publications

References 8 publications

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

State-of-the-Art Research on Motion Control of Maritime Autonomous Surface Ships

Energy efficient path planning for Unmanned Surface Vehicle in spatially-temporally variant environment

Ship Route Planning Based on Double-Cycling Genetic Algorithm Considering Ship Maneuverability Constraint

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