In this article, the three-dimensional trajectory tracking control of an autonomous underwater vehicle is addressed. The vehicle is assumed to be underactuated and the system parameters and the external disturbances are unknown. First, the five degrees of freedom kinematics and dynamics model of underactuated autonomous underwater vehicle are acquired. Following this, reduced-order linear extended state observers are designed to estimate and compensate for the uncertainties that exist in the model and the external disturbances. A backstepping active disturbance rejection control method is designed with the help of a time-varying barrier Lyapunov function to constrain the position tracking error. Furthermore, the controller system can be proved to be stable by employing the Lyapunov stability theory. Finally, the simulation and comparative analyses demonstrate the usefulness and robustness of the proposed controller in the presence of internal parameter uncertainties and external time-varying disturbances.

Terrain-aided navigation (TAN) is one of the most effective approaches for solving the long-range navigation of autonomous underwater vehicles. However, the positioning accuracy of TAN can be greatly influenced by the terrain information of matching areas. A TAN system might also fail catastrophically when the seabed topography changes. To address these problems, a dynamic path planning method for TAN, which includes environment modelling, offline path planning, and online re-planning, is proposed in this paper. Terrain standard deviation is applied to represent terrain information in environment modelling, and the Mahalanobis distance and roulette approach are introduced in the offline path planning approach. Furthermore, the topographic changes are identified and handled via an online re-planning approach. Simulation experiments are conducted, and comparisons are made with the A ^* algorithm. According to the experimental results, the proposed method improves the positioning accuracy of TAN during the mission, and its ability to deal with seabed topographic changes is also verified.

This article presents a navigation method for an autonomous underwater vehicle being recovered by a human-occupied vehicle. The autonomous underwater vehicle is considered to carry underwater navigation sensors such as ultra-short baseline, Doppler velocity log, and inertial navigation system. Using these sensors’ information, a navigation module combining the ultra-short baseline positioning and inertial positioning is established. In this study, there is assumed to be no communication between the autonomous underwater vehicle and human-occupied vehicle; thus, to obtain the autonomous underwater vehicle position in the inertial coordinate, a conjecture method to obtain the human-occupied vehicle coordinates is proposed. To reduce the error accumulation of autonomous underwater vehicle navigation, a method called one-step dead reckoning positioning is proposed, and the one-step dead reckoning positioning is treated as a correction to combine with ultra-short baseline positioning by a data fusion algorithm. One-step dead reckoning positioning is a positioning method based on the previous time-step coordinates of the autonomous underwater vehicle.

This paper investigates the patterns, causes, and implications of China's structural change and its contribution to China's regional growth. Among many other findings, our regression results show that conditional convergence exists across different regions in China. Regional structural change has a convergence effect and regional openness facilitates regional structural change. Structural shocks and structural transformation had the opposite effect on China's interregional convergence during the 1990s, though the combined effect of overall structural change is a convergence effect. We also find that Chinese regions rely more heavily on structural change for labor productivity growth as the economy evolves. In summary, the results of our empirical analysis support the hypothesis underlying the theoretical model of this paper.