At present, the track control of unmanned surface vessel (USV) is mainly divided into direct track control and indirect track control, both of which have their advantages in the aspect of USV track control. Though having high control precision, the former still fails to meet the related requirements. To tackle this problem, the nonlinear model predictive control (NMPC) algorithm was optimized based on the convolutional neural network (CNN) in order to design a direct USV track controller. In consideration of the nonlinearity and time lag problems that are likely to occur in USV track control, the convolutional neural network was used to solve the optimal control sequence of the predictive control algorithm. The simulation test indicates that this algorithm has effectively improved the accuracy and real-time performance of track control.

In terms of the reliability simulation assessment model of x-type mechanical equipment, in line with different failure modes of miscellaneous parts, the x-type mechanical equipment was decomposed into a compressor, combustion chamber, gas turbine, control system, and auxiliary equipment. A model for an operational task was established, with a navigation time of 90 days. The duration met the requirements of the crew-level maintenance and support system for replacing and maintaining the parts. As supposed that the typically scheduled maintenance cycle of the equipment is 36 months, the lifespan of the device is longer than ten years from the use to irreparable functional failures or the fault rate exceeding the acceptable range [1].

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