This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance.

In fractal theory, the fractal dimension has been accepted as a quantitative parameter to measure the complexity of fluctuations and the persistence of wind speeds. Typhoons are extreme wind events that damage structures. In this study, on the basis of wind field measurements, the fractal dimension characteristics of four typhoons in southeastern China are examined. Typhoon wind speeds at different heights and locations are presented. Monofractal dimension analysis was first conducted, and the results revealed that the typhoon wind speeds were persistent, with fractal dimensions smaller than 1.5. For all four typhoons considered, with the onset of landfall, the fractal dimensions approach 1.5, indicating unpredictable trends in the time series. Multifractality is accepted to exist in the typhoon wind speed dataset, and multifractal analysis was also conducted on the basis of the measured typhoon wind speeds. The results show that the fractal parameters calculated by multifractal analysis are generally greater than those calculated via monofractal analysis. This research aims to improve the understanding of the inner dynamic characteristics of typhoon wind speeds. These fractal parameters can provide quantitative references for future typhoon simulations and predictions.