Issue 1 (May)

Considering the issue of low control accuracy in joint trajectory tracking control for manipulator systems with actuator saturation due to external disturbances, modelling inaccuracies, and joint friction, a sliding mode active disturbance rejection control approach was proposed. An improved extended state observer is employed to observe and estimate the lumped disturbances affecting the system, providing feedback compensation. A variable gain reaching law is devised, coupled with a fast non-singular terminal sliding mode to design the system control law, which mitigates chattering inherent and ensuring precision control. Additionally, a novel output error compensation-based anti-windup scheme is introduced to compensate for the detrimental impacts caused by actuator saturation. Simulation results collectively demonstrate that the proposed tracking control algorithm exhibits better control performance and robustness against disturbances.

This study explores the use of simulated natural drainage systems for sustainable urban landscape planning to address flood disasters in low-lying urban areas. Traditional drainage methods lack sustainability, whereas simulated natural drainage systems, such as green infrastructure, rain gardens, and wetland parks, can enhance urban flood resilience and environmental quality through scientific design and strategic layout. In San Francisco, the issue of flood disasters in low-lying areas has become increasingly severe, with traditional drainage systems struggling to effectively manage flood risks under extreme weather conditions. This study employs R programming to simulate natural drainage systems, evaluating their effectiveness in mitigating flood disasters and improving ecological environments. By utilizing rainfall data, topographic data, and drainage system data from San Francisco, a simplified hydrological model was constructed to calculate catchment areas and runoff, simulating the effects of natural drainage systems. Results indicate that natural drainage systems significantly reduce flood risks and enhance ecological benefits. The findings provide urban planners with new flood management strategies, emphasizing the importance of integrated management and systemic thinking, and highlight the future potential for widespread application in urban planning. By integrating landscape design with water resource management principles, this study proposes a systematic solution validated through case studies in different cities, demonstrating its feasibility and effectiveness. This research offers new insights and methods for urban flood control engineering and landscape planning, contributing to enhanced urban disaster resilience and improved urban living environments. The simulation of natural drainage systems facilitates sustainable urban development, providing valuable references for the advancement of sustainable urban landscape planning.

With increasing coal mining depth, the likelihood of rock bursts has significantly risen, posing a major threat to coal mine safety in China. This paper aims to develop classification and prediction models to identify and predict rock burst precursor signals, thereby mitigating this hazard in deep mining. Using acoustic emission (AE) and electromagnetic radiation (EMR) data, we extracted time-frequency domain features and constructed decision tree and grey prediction models with a sliding window approach. For interference signal identification, we analyzed class C signals, determining their mean, kurtosis, and spectral peak values. For predicting class B signal trends, similar features were used to construct a decision tree model, which successfully identified precursor signals. The model demonstrated excellent classification performance in ROC curve tests. This research provides a scientific basis for preventing and controlling rock bursts, reducing engineering efforts, and enhancing coal mine safety.