In this research, based on the ultra-local model, a novel compound model-free control strategy with an intelligent Proportional-Integral and super-twisting Sliding Mode Control (MFiPISTSMC) strategy for permanent magnet synchronous motor (PMSM) drives is proposed. Firstly, an intelligent Proportional-Integral (iPI) control strategy is designed for motor speed regulation. Secondly, a super-twisting Sliding Mode Control (STSMC) strategy is constructed based on the ultra-local model of PMSM. At the same time, the unknown term of the ultra-local model of PMSM is estimated by a Linear Extended State Observer (LESO). The stability of the compound MFiPISTSMC strategy is proved by the Lyapunov stability theorem. As a result of the compound MFiPISTSMC strategy integrating the STSMC strategy, the iPI control strategy and the LESO is proposed to have excellent performance. Finally, the static characteristic, dynamic characteristic and robustness of the novel compound MFiPISTSMC strategy are verified by simulation and experimental results.

This paper proposes a novel model-free super-twisting nonlinear sliding mode control (MFSTNLSMC) strategy with an improved smoothing extended state observer (SESO) for permanent magnet synchronous motor (PMSM) drives. First of all, the improved SESO is introduced to estimate the unknown term of the PMSM ultra-local model. Secondly, a novel nonlinear sliding mode surface (NLSMS) is designed, which can effectively overcome the disadvantages of simple and rough signal processing of the conventional linear sliding mode surface. At the same time, a super-twisting (ST) structure is chosen to suppress the chattering phenomenon and improve system robustness. Then, the Lyapunov stability theorem is used to prove the stability of the proposed control algorithm. Finally, both comparative simulations and experimental demonstrations verify the excellent speed tracking performance and robustness of the proposed control strategy.

Based on the room-temperature phosphorescence (RTP) peculiarity of Mn-doped ZnS quantum dots (QDs), a bifenthrin sensor was fabricated by coupling molecular imprinted polymers (MIPs) on the surface of silane modified QDs via atom transfer radical polymerization (ATRP). The stepwise synthesis was realized with ZnS QDs-NH₂-Br as the initiator, methacrylic acid (MAA) as the functional monomer, divinylbenzene (DVB) as the cross-linker, CuBr as the catalyst and 2,2′-bipyridine as the complexant. The smart RTP probe for bifenthrin was gained by surface molecular imprinting technology (SMIT) that can not only produce the stable RTP intensity of Mn-doped ZnS QDs, but can also enable more efficient recognition of the target molecule. The results presented show that the detection limit of the probe was 16.7 μg L⁻¹. The RTP stability, selectivity and sensitivity of the bifenthrin sensor were evaluated by using a spectrophotometer. The high sensitivity was due to an electron-transfer mechanism between the target and the QDs, and the particular selectivity was due to SMIT. Therefore, this novel strategy combines RTP of Mn-doped ZnS QDs with ATRP to use the best aspects of both. It offers the advantages of straightforward preparation and processing, fast response, low toxicity and optical stability, so it is very promising for the detection of bifenthrin.

This paper presents a strategy for a thermal-structural test with quartz lamp heaters (TSTQLH), combined with an ultra-local model, a closed-loop controller, a linear extended state observer (LESO), and an auxiliary controller. The TSTQLH is a real time ground simulation of aerodynamic heating for hypersonic vehicles to optimize their thermal protection systems (TPS). However, lack of a system dynamic model for the TSTQLH results in inaccurate tracking of aerodynamic heating. In addition, during the control process, the TSTQLH has internal uncertainties of resistance and external disturbances. Therefore, it is necessary to establish a mathematical model between controllable $\alpha \left(t\right)$ and measurable $T_1\left(t\right)$. An ultra-local model of model-free control plays a crucial role in simplifying system complexity and reducing high-order terms due to high nonlinearities and strong couplings in the system dynamic model, and a global nonsingular fast terminal sliding mode control (GNFTSMC) is added to an ultra-local model, which is used to guarantee great tracking performance in the sliding phase and fast convergence to the equilibrium state in finite time. Moreover, the LESO is used mainly to estimate all disturbances in real time, and an adaptive neural network (ANN) shows a good approximation property in compensation for estimation errors by using a cubic B-spline function. The fitted curve of the wall temperature in the time sequence represents a reference temperature trajectory from the surface contour of an X-43A’s wing. The comparative results validate that the proposed control strategy possesses strong robustness to track the reference temperature trajectory.

This paper presents significant improvements in the accuracy and computational efficiency of safety helmet detection within industrial environments through the optimization of the you only look once version 5 small (YOLOv5s) model structure and the enhancement of its loss function. We introduce the convolutional block attention module (CBAM) to bolster the model’s sensitivity to key features, thereby enhancing detection accuracy. To address potential performance degradation issues associated with the complete intersection over union (CIoU) loss function in the original model, we implement the modified penalty-decay intersection over union (MPDIoU) loss function to achieve more stable and precise bounding box regression. Furthermore, considering the original YOLOv5s model’s large parameter count, we adopt a lightweight design using the MobileNetV3 architecture and replace the original squeeze-and-excitation (SE) attention mechanism with CBAM, significantly reducing computational complexity. These improvements reduce the model’s parameters from 15.7 GFLOPs to 5.7 GFLOPs while increasing the mean average precision (mAP) from 82.34% to 91.56%, demonstrating its superior performance and potential value in practical industrial applications.