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Consciousness Neural Network for Path Tracking Control of Floating Objects at Sea

The problem of tracking and controlling drifting objects on the sea is often faced with many problems, such as complex control environment, low stability of controller and a large number of algorithm calculation. In order to realize the accurate control of drifting objects on the sea, a course controller based on consciousness neural network technology is proposed. First of all, combined with Nash equilibrium guidance, based on the trajectory characteristics and control requirements of drifting objects, the tracking problem of drifting objects is modeled, and its state space, action space and reward function are designed. Then, the conscious neural network algorithm is used as the implementation of the controller, and the off-line learning method is used to train the controller. Finally, the trained controller is compared with the original controller BP-PID controller is compared to analyze the control effect. The simulation results show that the designed controller based on consciousness neural network can converge rapidly from the training process to meet the control requirements. Compared with BP-PID controller, the trained neural network has the advantages of fast tracking and small error. The research results can provide a reference for the path tracking control of drifting objects on the sea.