Autonomous underwater vehicle (AUV) has been employed in oceanography applications based on a reliable navigation. The complex underwater environment leads to more velocity measurement errors of AUV, so it is difficult to determine the accurate navigation and positioning information. To solve the problem, a novel variational Bayesian-based filter for inaccurate input (VBFII) is proposed to determine the state information under the complex marine condition of inaccurate input. Firstly, the velocities are assumed to follow the Gaussian distribution, which can better describe the model of input information. Secondly, the augmentation method is used to augment the state vector and error covariance matrix to simplify calculation. The augmented state vector, the augmented predicted error covariance and measurement error noise matrices are derived more accurately based on the variational Bayesian (VB) approach. The experiment results show that the proposed VBFII has better estimation accuracy and robustness than other comparison algorithms.

In order to solve the problem of SINS/DVL/USBL system interference in complex underwater environment. An asynchronous sequential robust filter method is proposed in this paper. The original output azimuth, slant range and altitude of the ultra-short baseline as the measurement information, a compact combination model is established. The problem of large amount of computation caused by the high measurement dimension of multi-sensor information fusion is solved by introducing asynchronous sequential filter technology. Meanwhile, a robust Kalman filter algorithm is proposed, which based on Mahalanobis distance. The outlier detection is designed based on statistical characteristics. Finally, the effectiveness of the method proposed in this paper is verified by simulation and River experiments. The experimental results show that the method proposed in this paper can effectively eliminate outliers and improve the navigation accuracy while reducing the amount of computation.

Continuous-time optimization is now an active research field and many valuable results have been proposed. As is known, a fast convergence speed is always desirable for an optimization algorithm and thus the fixed-time convergence in continuous-time optimization is considered in this letter. Different from existing fixed-time schemes, a novel fixed-time gradient method with a fractional adaptive gain is proposed, whose convergence time is determined by the first positive zero of a Mittag-Leffler function and independent of initial conditions. Furthermore, to avoid the possible singularity, two non-singular fixed-time gradient methods are proposed. Finally, all the results are extended to the second-order algorithm, whose convergence time is proven to be independent of both initial conditions and the shape of the target function.

For the SINS/DVL integrated navigation system, the installation error angle and DVL scale factor error are the key factors affecting the accuracy of the integrated navigation system. Aiming at this problem, based on the traditional Kalman filter calibration method, this paper discusses the estimation of calibration parameters under different models. On the one hand, according to the observability of state variables, an improved calibration model is proposed and the corresponding state equation and measurement equation are given. On the other hand, the traditional calibration model based on DVL three-dimensional velocity information is abandoned, DVL four-channel beam velocity information is introduced, a tight integrated calibration model is established, and a tight integrated model calibration method is proposed. Combined with the traditional calibration model and the two improved models in this paper, the River te st is designed. The experimental results show that the improved model proposed in this paper has better calibration accuracy than the traditional model. At the same time, the integrated navigation results based on SINS/DVL show that the model proposed in this paper has a smaller position error.

In the underwater navigation system, the Strap-down Inertial navigation (SINS)/Doppler Velocity Log (DVL) system has the advantage of autonomy. However, affected by the measurement range of DVL, there are two operating modes: bottom-track and water-track. To solve the navigation continuity problem of SINS/DVL under different modes, an SINS/DVL navigation method aided ocean current is proposed in this paper. On the one hand, according to different work modes of DVL, state space models based on bottom-track and water-track are established respectively. In order to improve the model match, DVL scale factor error and ocean current information are considered separately in this paper. On the other hand, in order to reduce the influence of complex noises, an improved Variational Bayes Adaptive Kalman Filter (VBAKF) algorithm is proposed. Based on VBAKF, a noise estimator is introduced, and an adaptive adjustment factor is designed. The improved VBAKF can estimate measurement noise and system noise simultaneously. The results of simulation, vehicle and river tests show that the proposed SINS/DVL method is effective and feasible. At the same time, the improved VBAKF algorithm can reduce the external noise interference and improve the navigation accuracy.

Independent pitch control (IPC) is a crucial technology for enhancing the performance of wind turbines, optimizing the power output, and reducing the loads by managing each blade. This paper explores the primary vibration modes of semi-submersible wind turbines under wind-wave coupling. Given the effectiveness of pitch control in vibration suppression, this paper addresses the limitations of conventional collective pitch control (CPC) by designing an independent pitch control method based on an equivalent wind speed model (EWIPC). This model constructs an effective representation of the actual wind speed’s influence on pitch angle by comprehensively considering the spatial distribution of wind speeds. This way, the control accuracy and response speed are significantly improved, making the control strategy more intuitive and efficient in complex wind speed environments. The proposed independent pitch control method is validated through simulations on the International Energy Agency (IEA) 15 MW wind turbine. The simulation results indicate that the EWIPC stabilizes wind turbine power output and reduces structural loads. Additionally, it demonstrates significant effectiveness in reducing vibrations of the blades and tower, as well as in eliminating 1P oscillations in the blade root bending moment.