Autopilot Design for Unmanned Surface Vehicle based on CNN and ACO

Dongming Zhao, Tiantian Yang, Wen Ou, Hao Zhou


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.


USV, autopilot, predictive control, Convolution Neural Network (CNN), Ant Colony Optimization (ACO), rolling optimization

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