Citrus has become a pivotal industry for the rapid development of agriculture and increasing farmers’ incomes in the main production areas of southern China. Knowing how to diagnose and control citrus huanglongbing has always been a challenge for fruit farmers. To promptly recognize the diagnosis of citrus huanglongbing, a new classification model of citrus huanglongbing was established based on MobileNetV2 with a convolutional block attention module (CBAM-MobileNetV2) and transfer learning. First, the convolution features were extracted using convolution modules to capture high-level object-based information. Second, an attention module was utilized to capture interesting semantic information. Third, the convolution module and attention module were combined to fuse these two types of information. Last, a new fully connected layer and a softmax layer were established. The collected 751 citrus huanglongbing images, with sizes of 3648 × 2736, were divided into early, middle, and late leaf images with different disease degrees, and were enhanced to 6008 leaf images with sizes of 512 × 512, including 2360 early citrus huanglongbing images, 2024 middle citrus huanglongbing images, and 1624 late citrus huanglongbing images. In total, 80% and 20% of the collected citrus huanglongbing images were assigned to the training set and the test set, respectively. The effects of different transfer learning methods, different model training effects, and initial learning rates on model performance were analyzed. The results show that with the same model and initial learning rate, the transfer learning method of parameter fine tuning was obviously better than the transfer learning method of parameter freezing, and that the recognition accuracy of the test set improved by 1.02~13.6%. The recognition accuracy of the citrus huanglongbing image recognition model based on CBAM-MobileNetV2 and transfer learning was 98.75% at an initial learning rate of 0.001, and the loss value was 0.0748. The accuracy rates of the MobileNetV2, Xception, and InceptionV3 network models were 98.14%, 96.96%, and 97.55%, respectively, and the effect was not as significant as that of CBAM-MobileNetV2. Therefore, based on CBAM-MobileNetV2 and transfer learning, an image recognition model of citrus huanglongbing images with high recognition accuracy could be constructed.

To solve the problem of system output voltage fluctuation caused by interferences such as load fluctuation and internal inductor parameter perturbation in a flyback converter, a second-order linear active disturbance rejection control (LADRC) strategy based on output voltage is proposed in this paper. A small-signal model of a CCM flyback converter is established, and the equivalent transfer function of voltage control based on second-order LADRC is derived. A second-order LADRC is constructed, and a parameter design method for the controller is proposed. The response characteristics of the output voltage of the converter under five internal and external disturbances of different control strategies are compared and studied using MATLAB R2022b/Simulink simulation software, and a CCM flyback converter experimental platform based on dSPACE is built to verify the corresponding comparative experiments. The simulation and experimental results jointly verify the superiority of the control strategy for the anti-interference and robustness of the output voltage of the CCM flyback converter.

To address the difficulty of simultaneously achieving fast response, high stability, and strong disturbance rejection in gimbal systems operating under complex conditions, an adaptive control strategy based on offline–online cooperative optimization is proposed. The method is built upon the conventional proportional–integral–derivative (PID) control framework. First, a particle swarm optimization (PSO) algorithm is employed offline to obtain an optimized set of initial control parameters, thereby improving the transient response during the startup phase. Subsequently, a single-neuron adaptive (SNA) mechanism is introduced to adjust the control parameters online according to real-time error information, enhancing the system’s adaptability to environmental variations and external disturbances. Stability analysis demonstrates the convergence of the proposed control scheme. Finally, an experimental gimbal platform is constructed to validate the effectiveness of the method. Experimental results show that, under various disturbance conditions, the proposed strategy effectively reduces angular fluctuation amplitude, shortens the settling time, and maintains smooth control performance. These results indicate that the proposed control strategy exhibits strong robustness and significant engineering applicability.

In this study, GaN devices are implemented in low-power three-phase inverters to achieve high-frequency operation and a compact design. A 500 W power-rated prototype operating at a switching frequency of 200 kHz was designed. A linear active disturbance rejection control (LADRC) strategy was introduced to solve the issue of high-frequency inverter oscillation. Additionally, the driving circuit was optimized to address the issue of voltage spikes in the drive voltage. Subsequently, the feasibility and effectiveness of the control strategy were validated through a system simulation model built in Simulink. Lastly, a digital control algorithm was developed by leveraging the capabilities of DSP28034. During testing at an operating frequency of 200 kHz, it was found that the drive voltage waveform was excellent and that the three-phase inverter output remained stable, with no signs of oscillation. The implemented design effectively suppresses peak drive voltage levels, demonstrating optimized gate drive circuitry performance. The output waveform of three-phase inverter proves the effectiveness of the control strategy.