In this study, an improved sliding mode control (ISMC) method combining continuous non-singular terminal sliding mode (CNTSM) and linear active disturbance rejection control (LADRC) for permanent magnet synchronous motor (PMSM) servo system is proposed to improve the performance of PMSM. Based on the mathematical model of PMSM, combined with SMC theory and LADRC theory, CNTSM controller based on continuous function is proposed to solve the singularity of terminal sliding mode (TSM) control. In order to solve the contradiction between the convergence performance of sliding mode control and the ability of chattering suppression, fuzzy control is introduced to adjust the gain of sliding mode controller in real time. LADRC is developed to ensure the current starting conditions of the motor and improve the robustness. Moreover, the asymptotic stability of the system is guaranteed by the Lyapunov theorem and it is verified by the AISim MBD servo motor experimental platform. Experiments show that the algorithm has good tracking performance and robustness.

To improve the accuracy of wind power prediction, a short-term wind power prediction model based on variational mode decomposition (VMD) and improved salp swarm algorithm (ISSA) optimized least squares support vector machine (LSSVM) is proposed. In the model, the variational modal decomposition is used to decompose the wind power sequence into multiple eigenmode components with limited bandwidth. The improved salp swarm algorithm is employed to tune the regularization parameter and kernel parameter in LSSVM. The proposed wind power prediction strategy using mean one-hour historical wind power data collected from a wind farm located in zhejiang, China. Compared with other prediction models illustrate the better prediction performance of VMD-ISSA-LSSVM.

Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models.

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