A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

Yang, Fang; Pang, Muye; Wang, Biao

doi:10.1016/j.procs.2017.06.035

Cited by 22 publications

(8 citation statements)

References 7 publications

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“…Fang et al. (2017) proposes a new course control based on the back‐propagation neural network to achieve more effective PID control for the USV. Furthermore, neural networks are often used in the control system of the USV to handle the environmental uncertainties caused by waves and currents (Peng et al., 2016; Shojaei, 2016; Woo et al., 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous vessels: state of the art and potential opportunities in logistics

Góez

Guajardo

et al. 2020

Int Trans Operational Res

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The growth in technology on autonomous transportation systems is currently motivating a number of research initiatives. This paper first presents a survey of the literature on autonomous marine vessels in general. By identifying the main research interests in this field, we define 10 thematic categories. The collected articles are then classified according to these categories. We show that research on autonomous vessels has increased dramatically in the past decade. However, most of the published articles have focused on navigation control and safety issues. Studies regarding other topics, such as transport and logistics, are very limited. Although our main interest is the literature on autonomous vessels, we contrast its development with respect to the literature on autonomous cars so as to have a better understanding about the future potentials in the research on autonomous vessels. The comparison shows that there are great opportunities for research about transportation and logistics with autonomous vessels. Finally, several potential research areas regarding logistics with autonomous vessels are proposed. As the technology behind remote‐controlled or autonomous ships is maturing rapidly, we believe that it is already time for researchers in the field to start looking into future water‐borne transport and logistics using autonomous vessels.

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Section: Literature Reviewmentioning

confidence: 99%

Autonomous vessels: state of the art and potential opportunities in logistics

Góez

Guajardo

et al. 2020

Int Trans Operational Res

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“…where β 0 , β 1 ,…,β m is the regression coefficient, є is a random variable (the remaining parameters), and x 1 , x 2 ,…,x m is the set of explanatory variables for the SRA model. The stepwise regression method was used for the model built by (8), and the explanatory variable set x m was significantly affected y. Variables can be added to or deleted from the regression model based on their significance, and this approach was used to confirm the explanatory variable set for the regression model. The steps of the stepwise regression algorithms were achieved as follows: duced in each step; in this manner, a partial regression equation can be established.…”

Section: Sra Model Buildingmentioning

confidence: 99%

“…Traditional data processing approaches typically fail in these applications and have relatively high costs. Due to its inherent capabilities, a BPNN can be successfully used to predict properties in a wide range of fields, such as chemistry [2,26,27], economics [6], medicine [15], engineering [8], psychology [25], and food quality assessment [1]. For instance, five different tea samples have been classified using a BPNN, achieving an accuracy rate of 88% [5].…”

Section: Introductionmentioning

confidence: 99%

A predictive model for the sensory aroma characteristics of flue-cured tobacco based on a back-propagation neural network

Chen

Kawaguchi

et al. 2020

SN Appl. Sci.

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In this study, aroma quality, aroma quantity, diffusiveness and offensive odor predictions were modeled using a back propagation neural network (BPNN). The model was built from chemical components and sensory quality data features of flue-cured tobacco leaf samples. The results showed that the BPNN model adapted the optimized hidden layer well and had excellent generalization ability and robustness. The coefficients of determination (R 2) for the indices of tobacco aroma characteristics based on the BPNN model, namely, aroma quality, aroma quantity, diffusiveness and offensive odor, were all above 0.7. The accuracies of the model parameters (mean squared error, regression mean standard error , and mean absolute error) were better than those of the model built using stepwise regression analysis. These results indicate that the BPNN prediction model is reliable and can accurately predict the sensory aroma characteristic qualities of flue-cured tobacco leaf.

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“…Classic PID algorithm was used as the main heading control algorithm, and back-propagation neural network (BPNN) was also utilized to achieve more effective self-adaptive PID control. 15 At the same time, in order to improve the convergence speed and precision of BPNN, artificial bee colony algorithm was introduced to minimize the error of system and adjust the weight of BPNN. 16 However, BPNN structure selection is difficult to determine, generally based on experience, besides, BPNN has the problem of sample dependence: the approximation and generalization ability of network model is closely related to the typicality of learning samples, and it is difficult to select typical samples from the problem to form training set.…”

Section: Introductionmentioning

confidence: 99%

Unmanned surface vessel heading control of model-free adaptive method with variable integral separated and proportion control

Jiang

Liao

Miao

et al. 2019

International Journal of Advanced Robotic Systems

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Based on model-free adaptive control theory, the heading control problem of unmanned surface vessels under uncertain influence is explored. Firstly, the problems of compact form dynamic linearization model-free adaptive control method applied to unmanned surface vessel heading control are analyzed. Secondly, by introducing proportional control and variable integral separation factor, an variable integral separation model-free adaptive control algorithm with proportional control is proposed. The introduction of proportional control and variable integral separation factor solves the problems of oscillation, instability, and integral saturation when rudder angle is controlled directly to control the heading of unmanned surface vessel with compact form dynamic linearization model-free adaptive control method. Finally, the effectiveness of the method is verified by the simulation and field experiments results of heading control with model perturbation and system time delay in unmanned surface vessel heading subsystem.

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A course control system of unmanned surface vehicle (USV) using back-propagation neural network (BPNN) and artificial bee colony (ABC) algorithm

Cited by 22 publications

References 7 publications

Autonomous vessels: state of the art and potential opportunities in logistics

Autonomous vessels: state of the art and potential opportunities in logistics

A predictive model for the sensory aroma characteristics of flue-cured tobacco based on a back-propagation neural network

Unmanned surface vessel heading control of model-free adaptive method with variable integral separated and proportion control

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