KEYWORDS: Control systems, Design, Control systems design, Magnetism, Process control, Device simulation, Signal processing, Complex systems, Algorithm development, Switches
With the wide use of Brushless DC Motor (BLDCM) in industry, traffic and home applications, higher requirements are put forward for the performance and stability of its control system. Although the traditional PID control method is simple and easy to use, it may not perform well in the face of nonlinear and time-varying BLDCM systems, especially in the face of external disturbances and parameter uncertainties. In order to improve the control effect of BLDMC system, this paper devotes itself to designing an advanced speed control system, which adopts sliding mode control as the main control strategy. SMC can effectively suppress uncertainties and external disturbances by introducing sliding mode surface, thus improving the robustness and dynamic performance of the system. However, the traditional sliding mode control will bring bad jitter phenomenon because of high frequency switching. In order to reduce the occurrence of jitter, this topic adopts the integral sliding mode method for motor speed control to ensure the accuracy and reliability of brushless DC motor control.
Aiming at the problems of slow labor efficiency and low precision of lithium battery defect detection, a new method based on shear wave threshold and K-means clustering segmentation was proposed. For the defect image, the ROI region of interest was first extracted to obtain the polar slice region containing only the defect parts. Then, the image denoising was carried out with the shear wave threshold and the histogram normalization was carried out with image enhancement processing to obtain the image with low noise and high contrast between the defect and the background. The defect was extracted by K-means clustering segmentation algorithm. Morphological corrosion and expansion operations were carried out to fill the defects. Finally, the prewitt edge detection operator was used to detect the defects and output the defect information. The experimental results show that the algorithm can detect metal leakage, damage, black spots and scratches very well, which is suitable for industrial production.
For the traditional algorithm to detect lithium battery defects, the missing rate is high and the speed is slow, an improved YOLOv7 algorithm was proposed. Firstly, CBAM attention mechanism is added to feature extraction part, which can enhance network's representation ability. Secondly, in the feature fusion part, ConvNeXt lightweight module was used to replace the original ELAN module to reduce the model's complexity. Finally, SPD module is added before the detection head at the output end to increase focus on smaller goals with surface defects of lithium batteries at the output end. The results show that the optimization algorithm can improve the accuracy and speed of the lithium battery. The proposed algorithm achieves a 92.7% detection accuracy, surpassing the original network by 2.1%.
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