Abstract: A closed-loop automatic control system for ship speed and a method are provided, including the following steps: obtaining feedback information through a detection feedback module, and generating a final effective main engine speed order nE* through an outer-loop control module; through the middle-loop control module, the main engine speed order nE* is compared with the main engine speed n by the speed deviation ?n, which uses PID and other control algorithms, the final effective main engine fuel quantity order LS* is generated; through the inner-loop control module, generating the fuel quantity deviation ?L between the main engine fuel quantity order LS* and fuel quantity feedback signal L into an optimized fuel quantity value S; and completing the closed-loop adjustment of fuel injection quantity (FIQ).

Abstract: Disclosed is a total servo drive IRT pos-sync and load self-adaptive jack-up cooperative control system, belonging to the field of marine engineering equipment. The total servo drive IRT pos-sync and load self-adaptive jack-up cooperative control system includes a HMI, a main controller, a tilt angle sensor, a leg position calculator and a series of control modules of each leg; where the jack-up, the tilt angle sensor, the main controller and the leg position calculator are sequentially connected; the main controller is further connected to the HMI for data exchange; and each leg corresponds to a series of control module and is connected to the leg position calculator. According to the present disclosure, double layer virtual master axis IRT pos-sync with dynamic redundant load self-adaptive fault-tolerant strategy for single leg multi-motors cooperative control are implemented.

Abstract: Provided is a method for adjusting lubrication optimization of a marine main engine cylinder based on computer vision, and belongs to the technical field of marine power systems. The method includes: establishing a marine main engine lubrication database for cylinder lubrication analysis and quality evaluation; collecting cylinder lubrication data of each current cylinder, and preprocessing cylinder lubrication image set data; performing image segmentation and cluster analysis on the preprocessed cylinder lubrication image set, and comparing the preprocessed cylinder lubrication image set with the marine main engine lubrication database to obtain feature recognition results of carbon deposition, dirts, scratches and a wear degree; and according to the feature recognition results, evaluating a cylinder lubrication quality to obtain evaluation grades, and adjusting cylinder lubrication optimization according to the evaluation grades.

Abstract: Provided is a method for adjusting lubrication optimization of a marine main engine cylinder based on computer vision, and belongs to the technical field of marine power systems. The method includes: establishing a marine main engine lubrication database for cylinder lubrication analysis and quality evaluation; collecting cylinder lubrication data of each current cylinder, and preprocessing cylinder lubrication image set data; performing image segmentation and cluster analysis on the preprocessed cylinder lubrication image set, and comparing the preprocessed cylinder lubrication image set with the marine main engine lubrication database to obtain feature recognition results of carbon deposition, dirts, scratches and a wear degree; and according to the feature recognition results, evaluating a cylinder lubrication quality to obtain evaluation grades, and adjusting cylinder lubrication optimization according to the evaluation grades.

Abstract: An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high-accuracy feedback adjustment are achieved.

Abstract: An intelligent course planning method and controller for unmanned surface vehicle are provided, an overall optimization from starting point to end point of course deflection angle is achieved based on an optimization principle of approximate dynamic programming; an optimization of minimization of the course deflection angle and a control signal is achieved based on a quadratic form cost function and a performance index by designing a virtual radial basis function neural network and a least square method, a target course expression is obtained, and a stability and a degree of convergence are ensured through a positive-definite constraint of the Hessian matrix of the performance index. Compared with the related art, an overshoot of course deflection and the control signal is reduced, an optimization of flight and steering energy consumption is achieved, and completely data-driven for intelligent course planning for unmanned surface vehicle and a high- accuracy feedback adjustment are achieved.