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Supercavitation technology can greatly improve the speed of underwater vehicles by reducing drag. However, the time-delay characteristic of a supercavitating vehicle presents control challenge. In this paper, a linear parameter-varying (LPV) time-delay model for a supercavitating vehicle is established by analysing the time-delay characteristics of the planing force. Moreover, considering that the cavitation radius is easily perturbed by external disturbances, a supercavitating LPV delay model depicted as a polytope is established. Based on the model, a predictive controller based on time-delay characteristics is designed. The controller can solve linear matrix inequalities (LMI) online to find the optimal solution at a given moment. In addition, the controller introduces a different Lyapunov function and multiple free control variables to reduce the system's conservativeness and expand the system's initial feasible region, which can improve the control performance of the system. A simulation verifies that the system exhibits good stability and robustness.

Object extraction is an important tool in many applications within the image processing and computer vision communities. You Only Look Once version 3 (YOLOv3) has been extensively applied to many fields as a state-of-the-art technique for object semantic detection. Despite its numerous characteristics, YOLOv3 has to be combined with appropriate image segmentation technologies to achieve effective 2D object extraction in real-time monitoring, robot navigation, and target search. In this article, the K-means algorithm is applied to the segmentation of depth images. Considering the inherent sensitivity to the randomness of the initial cluster center and the uncertainty of cluster number K in the initialization phase of the K-means algorithm, this article proposes a new method that combines the semantic image information with the image depth information. Specifically, this method proposed to pre-classify the center depth of the object to determine the appropriate value of K required in the K-means algorithm. At the same time, the proposed algorithm improves the selection of the initial center via the maximin method. This article introduces a multi-parameter extraction method to enable to correctly identify the object of interest after image segmentation. The technique considers three parameters to achieve this: i) the elements of size, ii) the connected domain, and iii) the diagonal detection. Experiments using open-source datasets demonstrate that the average processing time and the segmentation accuracy of the improved K-means algorithm are 20.36% faster and 3.12% higher than the conventional K-means algorithm, respectively. The extraction accuracy of the proposed method is 6.69% higher than that of the SuperCut extraction method.