There is a growing concern to design intelligent controllers for autopiloting unmanned surface vehicles as solution for many naval and civilian requirements. Traditional autopilot’s performance declines due to the uncertainties in hydrodynamics as a result of harsh sailing conditions and sea states. This paper reports the design of a novel nonlinear model predictive controller (NMPC) based on convolutional neural network (CNN) and ant colony optimizer (ACO) which is superior to a linear proportional integral-derivative counterpart. This combination helps the control system to deal with model uncertainties with robustness. The results of simulation and experiment demonstrate the proposed method is more efficient and more capable to guide the vehicle through LOS waypoints particularly in the presence of large disturbances.
Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO
2018-05-27
doi:10.15837/ijccc.2018.3.3236
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol 13 No 3 (2018): International Journal of Computers Communications & Control (June); 429-439 ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2018.3
Article (Journal)
Electronic Resource
English
DDC: | 629 |
Autopilot design for a robotic unmanned surface vehicle
IEEE | 2015
|Autopilot Design for a Robotic Unmanned Surface Vehicle
TIBKAT | 2015
|Full range autopilot design for an unmanned underwater vehicle
British Library Conference Proceedings | 1997
|Adaptive Autopilot Design for Unmanned Airship
British Library Conference Proceedings | 2004
|An Autopilot Based on a Local Control Network Design for an Unmanned Surface Vehicle
Online Contents | 2012
|