In order to enhance the dynamic performance and anti-disturbance ability of speed control for a permanent magnet synchronous motor (PMSM), a sliding mode control method based on a PID sliding surface and an improved terminal sliding mode reaching law (ITSMRL) is proposed. Firstly, an ITSMRL is proposed to increase the reaching speed and reduce chattering; moreover, it has been verified that the reaching law (RL) can achieve a sliding mode surface in finite time. Then, based on the dynamic model of PMSMs with uncertainties, an extended state observer (ESO) is used to estimate the lumped disturbance, and it is proven that the estimated error is bounded. Finally, on the basis of the observed feedforward disturbance, to enhance the disturbance rejection ability of PMSMs, a controller that combines the PID sliding mode surface and the ITSMRL is proposed. Moreover, the stability of the closed-loop system is proven. The composite method has the characteristics of a fast reaching speed, small chattering and strong robustness, and is verified by experiments.

As critical components of wind energy systems, the structural integrity of wind turbine blades is directly tied to the operational safety and economic performance of wind turbines. With blade designs trending toward larger and more flexible structures and operating environments becoming increasingly harsh, maintenance strategies must urgently shift from reactive approaches to predictive maintenance paradigms. From an engineering application perspective, this study conducts a systematic and critical review of non-destructive testing (NDT) and structural health monitoring (SHM) technologies for wind turbine blades. Drawing on the literature published over the past decade, we examine the field applicability, limitations, and engineering challenges of core NDT techniques—including vision-based methods, acoustic approaches, vibration analysis, ultrasound, and infrared thermography. Particular emphasis is placed on the integration of data-driven approaches with engineering practice, evaluating the role of machine learning in fault classification and anomaly diagnosis, as well as the contributions of deep learning to automated defect detection in image and signal data. Moreover, this paper critically discusses the growing use of robotic inspection platforms, such as unmanned aerial vehicles and climbing robots, as multi-sensor carriers enabling rapid and comprehensive blade assessment. By comparatively analyzing detection performance, cost, and automation levels across technologies, we identify key engineering barriers, including environmental noise robustness, signal attenuation within complex blade structures, and the persistent gap between laboratory methods and field deployment. Finally, we outline forward-looking research directions, encompassing multi-modal sensor fusion, edge computing for real-time diagnostics, and the development of standardized SHM systems aimed at supporting full lifecycle blade management.

Ocean wave height plays an important role in the operation status of ocean wave energy conversion systems. In this paper, the future continuous ocean wave height within 2~3 s is forecasted by three methods, the autoregressive moving average model (ARMA) method, backpropagation (BP) neural network method, and radial basis function (RBF) neural network method. Then, the error between suggested forecast results and corresponding measured results are compared by the root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient R. The comparison result indicates that the RBF neural network method is preferred to the other two methods, having the advantage of high accuracy. Lastly, the reasons for the errors of the three forecasting methods are analyzed. This study signifies that the RBF neural network method is beneficial to the operation control and efficiency improvement of ocean wave energy conversion systems.