The sampled-data admissibility problem for nonlinear singular systems in Takagi–Sugeno fuzzy models is discussed. By adding some novel terms, a novel fuzzy time-dependent Lyapunov–Krasovskii functional is proposed to fully capture the available characteristics of the actual sampling pattern. Sufficient conditions are derived to determine the regularity, absence of impulses and asymptotic stability of the system by using Lyapunov stability theorems. Then, the fuzzy sampled-data controller is obtained by analysing the admissible condition. One practical example involving a truck–trailer system is considered. It is shown that the proposed method and the designed controller for the system are effective and that less conservativeness can be obtained.

To improve the accuracy of ship track prediction, the improved Grey Wolf Optimizer (GWO) and Support Vector Regression (SVR) models are incorporated for ship track prediction. The hunting strategy of dimensional learning was used to optimize the move search process of GWO and balance exploration and exploitation while maintaining population diversity. Selection and updating procedures keep GWO from being stuck in locally optimal solutions. The optimal parameters obtained by modified GWO were substituted into the SVR model to predict ship trajectory. Dimension Learning Grey Wolf Optimizer and Support Vector Regression (DLGWO-SVR), Grey Wolf Optimized Support Vector Regression (GWO-SVR), and Differential Evolution Grey Wolf Optimized Support Vector Regression (DEGWO-SVR) model trajectory prediction simulations were carried out. A comparison of the results shows that the trajectory prediction model based on DLGWO-SVR has higher prediction accuracy and meets the requirements of ship track prediction. The results of ship track prediction can not only improve the efficiency of marine traffic management but also prevent the occurrence of traffic accidents and maintain marine safety.

This paper investigates the stability analysis and the fuzzy dynamic positioning (DP) controller design for an autonomous surface vehicle (ASV) based on sampled-data. Firstly, the Takagi-Sugeno (T-S) fuzzy model for the ASV with dynamic positioning system (DPS) is established. Then the criteria for asymptotically stability analysis and controller synthesis are provided by means of linear matrix inequalities. And less conservative results can be obtained by introducing convex reciprocal inequalities. Finally, simulation result is shown that the fuzzy sampled-data controller is effective to guarantee that the states of the ASV are stable and have good DP performance under the external disturbance.