Resumen
In order to realize accurate and fast firefighting at sea, a control method for water cannons of unmanned fireboats considering wind and ship motion disturbances is presented. This method combines information fusion, computer vision, and prediction technology based on neural network. Firstly, a prediction model of the jet trajectory of the fire water cannon considering the disturbances of constant horizontal wind is established, and the effective range of the water cannon?s angles under the target working environment is obtained. Secondly, fusing the visual recognition information and predicted ship motion attitudes information, a double adaptive fuzzy controller is designed to compensate for the disturbances caused by the change in ship motion attitudes. Meanwhile, the online particle swarm optimization (PSO) is applied to fuzzy control to improve operational accuracy while enhancing the ability to adapt to environmental changes. The proposed control method was experimentally verified. As a result, the adaptive fuzzy controller based on the PSO can self-adjust the parameters to adapt to the changes in the working environment within 0.6 s, and the efficiency is improved by about 20%~50% compared with the traditional fuzzy control. The double adaptive fuzzy control can reach a stable and effective working state within 10 simulation steps, and the RMSE of the drop point error is only 3 ×10−3" role="presentation" style="position: relative;">×10-3×10-3
×
10
-
3
m for 40 simulation steps after stabilization, which can effectively resist disturbances and improve efficiency and control accuracy. The control method can provide a practical reference for engineering applications of water cannon control of unmanned fireboats.