The stability of autonomous liquid transporter unmanned aerial vehicles (UAVs) is crucial for precision in agricultural field spraying. This paper proposes an adaptive robust control scheme designed to stabilise agricultural quadrotor UAVs affected by variable mass and liquid sloshing dynamics during pesticide application. A comprehensive nonlinear mathematical model is developed to explicitly represent UAV-liquid interactions, accounting for both liquid sloshing and time-dependent mass variations. An adaptive PI-based sliding-mode controller is proposed, featuring a neural network identifier to estimate unknown sloshing forces and a nonlinear disturbance observer to mitigate external disturbances. Theoretical analysis through designed Lyapunov functions confirms the closed-loop system's robustness, stability, and bounded estimation errors, even despite varying liquid mass conditions. Simulation experiments validate the effectiveness of the proposed method, achieving approximately 41.4% and 53.8% in trajectory tracking accuracy, stabilisation, and disturbance rejection along the x-and y-axes, respectively, compared to the conventional proportional integral derivative (PID) counterpart. Overall, this research enhances UAV control methodologies by effectivelymanaging sloshing-induced dynamics, thereby facilitating improved quadrotor system designs and making a substantial contribution to agricultural automation.
The health status of apple leaves directly affects the quality and yield of apples, with foliar diseases posing a serious threat to overall production. Therefore, there is an urgent need for a rapid and accurate method for the classification and identification of apple leaf diseases to enable early and automated detection. This study utilises the Plant Pathology 2020 dataset and proposes a novel classification framework that integrates advanced vision transformers (ViTs) with convolutional neural networks (CNNs), enhanced by adaptive data augmentation strategies to improve classification performance. To address the inherent issue of class imbalance within the dataset, the synthetic minority oversampling technique (SMOTE) is employed to effectively augment the minority class samples. The proposed framework adopts a multi-stage training process to enhance feature representation and model robustness, enabling the effective capture of both local lesion patterns and global leaf characteristics. Experimental results demonstrate the stability and high performance of the proposed method, achieving a classification accuracy of 98.96%. Compared with traditional CNN architectures such as ResNet-50, DenseNet-121, GoogLeNet, InceptionV3, and VGG16, the proposed method exhibits significant improvements in both accuracy and generalisation across various augmented datasets. The findings indicate that the framework offers a feasible, reliable, and scalable solution for plant disease detection in the context of precision agriculture.
Elevator group control systems (EGCSs) are designed to efficiently manage three or more elevators for passenger transport. Most EGCS utilise hall call assignment methods to allocate elevators in response to passenger requests. Traditional EGCS algorithms typically exhibit an average waiting time (AWT) ranging from 20 to 60 s, a maximum waiting time (MaxAWT) exceeding 60 s, and a long-term waiting rate for passengers (LWP) greater than 10%. To enhance efficiency, a hall call assignment method based on the fuzzy EGCS (FEGCS) is proposed. The input variables for the evaluation index parameters of the FEGCS fuzzy inference are employed for elevator dispatching. A fuzzy inference mechanism is established for each evaluation factor, and the optimal elevator dispatching scheme is selected based on weighted criteria. Through MATLAB simulation evaluation, it is demonstrated that, compared to particle swarm optimisation (PSO), backpropagation neural network (BP), and fuzzy control algorithm (FCA), the combination of FCA and BP can reduce AWT by 7.5% to 24%, MaxAWT by 8% to 37.4%, and LWP by 16.7% to 100%.
