On the speed control for automated surface vessel operation

Kumarawadu, Sisil; Kumara, K.

doi:10.1109/iciafs.2007.4544790

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Cited by 10 publications

(4 citation statements)

References 6 publications

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“…The translational velocity and orientation angle are often set as the input control parameters of the basic motion control system for high-speed USVs. The motion of high-speed USVs is realized by direction and speed control (Yu et al, 2008;Bao et al, 2009;Kumarawadu and Kumara, 2007;Miskovic et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

et al. 2015

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Section: Introductionmentioning

confidence: 99%

Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

et al. 2015

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“…In Naeem et al (2006), the course controller is designed by combining classical PID control technology and fuzzy control technology, and it is verified by simulation, and good control effect is obtained. In Kumarawadu and Kumara (2007), the speed control of an unmanned vehicle under the influence of longitudinal, lateral and course coupling has been studied. At the same time, in order to ensure the stability of course motion, a speed controller based on Lyapunov function was designed.…”

Section: Introductionmentioning

confidence: 99%

Research on the course control of USV based on improved ADRC

et al. 2020

Systems Science & Control Engineering

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“…While course control is a prerequisite to solve the trajectory tracking, autonomous navigation and collision avoidance and other issues. So, Steering control is one of the important research topics in the field of motion control [2] for USV.…”

Section: Introductionmentioning

confidence: 99%

RBF Neural Network Control for USV with Input Saturation

Deng

Wang

et al. 2018

MATEC Web Conf.

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Intelligent control for USV with input saturation based on RBF network was proposed. Firstly, sliding surfaces with integral were designed on the basis of the sliding mode variable structure control technology. Secondly, RBF network was applied to approximate compensate the input saturation of system, and which was optimized by Genetic Algorithms. Finally, the control algorithm for USV was deduced by backstepping method with Lyapunov theory on the basis of sliding mode control. Relevant simulations show the control method is available for USV motion control.

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On the speed control for automated surface vessel operation

Cited by 10 publications

References 6 publications

Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

Local reactive obstacle avoidance approach for high-speed unmanned surface vehicle

Research on the course control of USV based on improved ADRC

RBF Neural Network Control for USV with Input Saturation

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