Performance Optimization of Ship Course Via Artificial Neural Network and Command Filtered Cdm-Backstepping Controller

Autor:	Fouad Haouari, Mohamed Tadjine, R. Gouri, Mohamed Seghir Boucherit, Nourdine Bali
Rok vydání:	2019
Předmět:	0209 industrial biotechnology 020901 industrial engineering & automation Artificial neural network Computer science Control theory 0103 physical sciences Backstepping controller 02 engineering and technology 010301 acoustics 01 natural sciences Course (navigation)
Zdroj:	Technological Engineering. 16:5-10
ISSN:	2451-3156 1336-5967
Popis:	This paper proposes a robust nonlinear ship course controller, under the control of which the system is globally asymptotically stabilized with high control quality. The proposed controller is synthesized by combining coefficient diagram method and command filtered backstepping based on first order filter to avoid the complex analytic derivation of the virtual control, the controller parameter are tuned using radial basis function neural network, It can not only obtain a higher accuracy in ship course controlling, but also infinitely approach the nonlinear system with quicker and more stable convergence. The simulation results illustrate that the projected controller shortens the settling time evidently with good system stability. It has a better performance than the traditional controllers.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::18b420dc35588be7e7b5185c90daeea5 https://doi.org/10.1515/teen-2019-0001 Zobrazit plný text záznamu