To improve the observation accuracy and robustness of the sensorless control of an interior permanent magnet synchronous motor (IPMSM), a sliding mode observer based on the super twisting algorithm (STA-SMO) with adaptive parameters estimation control is proposed, as parameter mismatches are considered. First, the conventional sliding mode observer (CSMO) is analyzed. The conventional exponential approach law produces a large chattering phenomenon in the back EMF estimation, which causes a large observation error when filtering the chattering through the low-pass filter. Second, a high-order approach law of the super twisting algorithm is introduced to observe the rotor position and speed estimation, which uses the integral function to eliminate the chattering of the sliding mode. Third, an adaptive parameter estimation control (APEC) is presented to enhance the observation accuracy caused by parameter mismatches; the motor parameter adaptive law of the APEC is designed by Lyapunov’s stability law. Finally, the proposed method not only reduces both the chattering and the low-pass filter, but it also enhances accuracy and robustness against parameter mismatches, as discussed through simulations and experiments.

Purifying soybean seed is greatly beneficial for improving the quality of soybean planting and products. Since the seeds are usually evaluated in many fields for sowing and oilseed processing, they must be identified quickly and accurately for select

It is of great significance to identify soybean seeds with high oil content since the oil content of soybean seeds decides oil yield. At present, related researches mostly used machine learning algorithm to identify soybean varieties with small samples. In this study, 5800 spectral data samples of 58 varieties in the range of 400–1000 nm were obtained. An acceptable method that combines hyperspectral imaging with one-dimensional convolutional neural network was proposed to distinguish high oilcontent soybean seeds. Moreover, traditional machine learning models, including support vector machine, k-nearest neighbor algorithm, and partial squares discriminant analysis, were also established in the experimental study. The effects of four preprocessing methods, namely moving window smoothing, standard normal variate, multivariate scattering correction, and Savitzky–Golay, were compared when building support vector machine-based identification models. The results showed that the model using multivariate scattering correction gave better test accuracy (94.5%), indicating that for this study, multivariate scattering correction was a more suitable method than others. Meanwhile, the study compared the performance of the four models by expanding the number of samples. The results showed that the proposed one-dimensional convolutional neural network model was more stable. The average accuracy of the training set and test set was 96% and 93%, respectively. Therefore, hyperspectral data combined with one-dimensional convolutional neural network was effective in identifying soybean seeds with high oil content.

The behaviours of the pig are often closely related to their health. Pig recognition is very important for pig behaviour analysis and digital breeding. Currently, the early signs and abnormal behaviours of sick pigs in breeding farms are mainly completed by human observation. However, visual inspection is labour intensive and time-consuming, and it suffers from the problems of individual experiences and varying environments. An improved ResNet model was proposed and applied to detect individual pigs in this study based on deep learning knowledge. The developed model captured the features of pigs applying across layer connections, and the ability of feature expression was improved by adding a new residual module. The number of layers was reduced to minimise the net complexity. Generally, the ResNet frame was developed by reducing the number of convolution layers, constructing different types of the residual module and adding the number of convolution kernels. The training accuracy and testing accuracy reached 98.2% and 96.4%, respectively, when using the improved model. The experiment results showed that the method proposed in this paper for checking living situations and disease prevention of commercial pigs in pig farms is potential.