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Article

Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles

1
Navigation College, Jiangsu Maritime Institute, Nanjing 211170, China
2
Maritime College, Hainan Vocational College of Science and Technology, Haikou 571126, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2020, 8(3), 210; https://doi.org/10.3390/jmse8030210
Received: 14 February 2020 / Revised: 13 March 2020 / Accepted: 14 March 2020 / Published: 18 March 2020
(This article belongs to the Special Issue Unmanned Marine Vehicles)
To improve the tracking stability control of unmanned surface vehicles (USVs), an intelligent control algorithm was proposed on the basis of an optimized radial basis function (RBF) neural network. The design process was as follows. First, the adaptation value and mutation probability were modified to improve the traditional optimization algorithm. Then, the improved genetic algorithms (GA) were used to optimize the network parameters online to improve their approximation performance. Additionally, the RBF neural network was used to approximate the function uncertainties of the USV motion system to eliminate the chattering caused by the uninterrupted switching of the sliding surface. Finally, an intelligent control law was introduced based on the sliding mode control with the Lyapunov stability theory. The simulation tests showed that the intelligent control algorithm can effectively guarantee the control accuracy of USVs. In addition, a comparative study with the sliding mode control algorithm based on an RBF network and fuzzy neural network showed that, under the same conditions, the stabilization time of the intelligent control system was 33.33% faster, the average overshoot was reduced by 20%, the control input was smoother, and less chattering occurred compared to the previous two attempts. View Full-Text
Keywords: intelligent computing; RBF neural network; genetic algorithms; sliding mode control; USV; optimized intelligent computing; RBF neural network; genetic algorithms; sliding mode control; USV; optimized
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MDPI and ACS Style

Wang, R.; Li, D.; Miao, K. Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles. J. Mar. Sci. Eng. 2020, 8, 210. https://doi.org/10.3390/jmse8030210

AMA Style

Wang R, Li D, Miao K. Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles. Journal of Marine Science and Engineering. 2020; 8(3):210. https://doi.org/10.3390/jmse8030210

Chicago/Turabian Style

Wang, Renqiang, Donglou Li, and Keyin Miao. 2020. "Optimized Radial Basis Function Neural Network Based Intelligent Control Algorithm of Unmanned Surface Vehicles" Journal of Marine Science and Engineering 8, no. 3: 210. https://doi.org/10.3390/jmse8030210

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