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Appl. Sci. 2018, 8(6), 926; https://doi.org/10.3390/app8060926

Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle

School of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
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Received: 21 April 2018 / Revised: 25 May 2018 / Accepted: 1 June 2018 / Published: 4 June 2018
(This article belongs to the Section Computer Science and Electrical Engineering)
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Abstract

In recent years, with the development of unmanned platforms, unmanned surface vehicles (USV) are attracting more and more attention. Compared to ordinary ships, USV have a smaller volume and faster speed, so their collision avoidance system (CAS) should have better responsiveness and stability. The paper describes a method that is based on finite control set model predictive control (FCS-MPC). A finite control set is generated by more practical control commands: the thruster speed and propulsion angle of the USV. The method is conceptually and computationally simple and yet quite versatile, as it can account for the dynamics of the USV, steering and propulsion system. Based on the theory of FCS-MPC, a safe and fast CAS is proposed, and it is verified in different static and dynamic environments. The real environment model for collision avoidance is established by extracting the environment data from the electronic chart. The result shows that the method is effective and can control the USV to sail safely and quickly in complex real scenarios with multiple dynamic obstacles. View Full-Text
Keywords: unmanned surface vehicle; collision avoidance; finite control set; model predictive control unmanned surface vehicle; collision avoidance; finite control set; model predictive control
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Sun, X.; Wang, G.; Fan, Y.; Mu, D.; Qiu, B. Collision Avoidance Using Finite Control Set Model Predictive Control for Unmanned Surface Vehicle. Appl. Sci. 2018, 8, 926.

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