Port loading automation systems can improve the efficiency of cargo transfer, save port operation time and create greater economic benefits. The recognition of ship hatch is the basis and premise of building an automatic loading system, it is a major time cost in the loading system meanwhile. How to identify the hatch quickly and accurately is an important problem that needs to be solved urgently under the actual production needs of ports. In order to save the time of ship hatch recognition, this paper proposes a fast hatch recognition algorithm based on point cloud contour extraction. The ship point cloud model generated by lidar scanning is preprocessed to remove the noise and isolated points in the model. Projecting the preprocessed point cloud on the XOY plane, converting the three-dimensional point cloud into a two-dimensional image, extracting the outline further getting the point cloud pixels with linear features of the two-dimensional image by [Formula: see text] algorithm. Projecting the feature point cloud to the X-axis to classify the hatches. According to the center point of each class of point cloud, searching for the nearest neighbor hatch edge feature points to calculate hatch coordinates in the world frame. An experimental study was carried out on the scan data of actual docked ships at Guoneng Tianjin Port. The results show that the algorithm can realize the hatch recognition quickly which the speed is increased by 424% compared with the previous algorithm and the identification accuracy meets the actual production needs of the port.

To solve the attitude tracking control problem of small unmanned helicopters under unknown bounded disturbances, an attitude tracking backstepping controller based on adaptive radial basis function (RBF) neural network disturbance compensation is proposed in this paper. The unknown disturbance is estimated online and in real time through RBF neural network with a novel gradient descent weight update rate. A novel attitude tracking backstepping controller based on virtual control variables is designed, and the system stability is analyzed using the Lyapunov method. The experimental verification of the backstepping controller is carried out on the three-degrees-of-freedom helicopter. The experiments show the effectiveness and advancedness of the proposed adaptive neural network backstepping controller in suppressing unknown external disturbances compared with the robust adaptive integral backstepping.

The anti-disturbance control of quadrotor attitude tracking under saturation constraints is a difficult problem. In this paper, a neural network-based model predictive controller for quadrotor systems with input saturation and external disturbances is developed. The unmodeled dynamics and external disturbances of the system are simplified to the disturbance superimposed on the nominal system, and the gradient descent neural networks are used to complete the estimation and compensation of the disturbance. The adaptive model predictive controller is designed based on the nominal system. The disturbance value estimated by the neural network adaptively adjusts the control constraints in the model predictive controller. The robustness and anti-disturbance of the designed controller are analyzed. The experiments show that, compared to the robust model predictive control, the algorithm proposed in this paper reduces the steady-state mean errors of the yaw, pitch, and roll attitude channels of the Hover system. Specifically, the algorithm results in a decrease of 2.622%, 2.292%, and 1.192% without external disturbances and 2.056%, 4.17%, and 0.956% with outside disturbances. Experimental results confirm the effectiveness of the proposed method.