The uniformity of the travel speed of electric reel sprinkling irrigation machines is a key factor affecting irrigation quality. However, conventional PID control is susceptible to sudden disturbances under complex farmland conditions, leading to reduced speed uniformity. To enhance the robustness of the control system, it is necessary to investigate new disturbance rejection control algorithms and their effects. Therefore, a kinematic model of the reel sprinkling irrigation machine and a brushless DC (BLDC) motor model were established, and a linear active disturbance rejection control (LADRC) strategy based on improved particle swarm optimization (IPSO) was proposed. The simulation results show that under variable speed conditions, the system exhibits no overshoot, with an adjustment time of 0.064 s; under variable load conditions, the speed vibration amplitude is less than 0.3%. The field test results indicate that at travel speeds of 10 m/h and 30 m/h, the maximum absolute deviation rate under IPSO-LADRC control is reduced by 27.07% and 13.98%, respectively, compared to PID control. The control strategy based on IPSO-LADRC effectively improves the control accuracy and robustness under complex farmland conditions, providing a reference for enhancing the control performance of other electric agricultural machinery.

The drip emitter is a fundamental component of the drip irrigation system, and its performance directly influences the efficiency of water–fertilizer–gas (WFG) coupling irrigation. However, the precise mechanism through which WFG coupling affects emitter clogging and system uniformity remains unclear. To address this, this study conducted a hydraulic performance test of the drip irrigation system based on micro-nano aerated drip irrigation technology. The clogging patterns of emitters and system uniformity were compared and analyzed under non-aerated drip irrigation and WFG coupling drip irrigation conditions. The results indicate that WFG coupling significantly alters the micromorphological structure and microbial diversity of clogged emitters. This change reduces clogging and can delay the clogging process of different types of emitters, thereby extending their service life by up to 29%. Additionally, it effectively improves the uniformity of the drip irrigation system. These findings highlight the potential of WFG coupling as an effective strategy to mitigate emitter clogging and optimize drip irrigation system performance.

In order to balance power supply and demand, which is crucial for the safe and effective functioning of power systems, short-term power load forecasting is a crucial component of power system planning and operation. This paper aims to address the issue of low prediction accuracy resulting from power load volatility and nonlinearity. It suggests optimizing the number of hidden layer nodes, number of iterations, and learning rate of bi-directional long- and short-term memory networks using the improved sparrow search algorithm, and predicting the actual load data using the load prediction model. Using actual power load data from Wuxi, Jiangsu Province, China, as a dataset, the model makes predictions. The results indicate that the model is effective because the enhanced sparrow algorithm optimizes the bi-directional long- and short-term memory network model for predicting the power load data with a relative error of only 2%, which is higher than the prediction accuracy of the other models proposed in the paper.