This invention discloses an
intelligent control optimization method for ship
hybrid propulsion based on
deep neural networks. By setting a generalized force control target, constructing a
supervised training dataset, and establishing a deep neural
network model with an
encoder and decoder structure, the
encoder maps the target generalized force to the thrust and angle commands of the
propeller. The decoder outputs the generalized force inversely based on the control commands. During training, a multi-objective
loss function with six sub-items is introduced to construct the physical constraints related to the
propeller into the form of a
loss function. The optimal
propeller command that satisfies various constraints is found. The model receives real-
time control command input and can quickly output propeller control commands that meet the constraints, realizing optimized thrust allocation. While ensuring that the physical constraints of the propeller are met, the method balances thrust allocation accuracy and
energy consumption, accurately tracks while reducing
energy consumption and excessive use of actuators, solves the ship thrust allocation problem under complex constraints, and efficiently completes the ship trajectory tracking task.