Drawing from recent developments in the field, this article explores advanced control methodologies for active suspension systems with the aim of enhancing ride comfort and vehicle handling. The study systematically and comprehensively implements, simulates, and compares five control methods: Proportional‐integral‐derivative (PID), linear quadratic regulator (LQR), , , and synthesis in the context of half‐vehicle active suspension systems. By using a detailed system model that includes parameter uncertainties and performance weights, analysis, and simulations are conducted to evaluate the performance of each control approach. The results provide valuable insights into the strengths and limitations of these methods, offering a comprehensive comparative analysis. Notably, the study reveals that control may not ensure stability for all possible combinations within a broad range of uncertainties, indicating the need for careful consideration in its application. The results and simulations thoroughly evaluate and compare the performance of each control strategy across various output responses, contributing to the advancement of more effective and reliable active suspension systems.

As autonomous drone swarms become increasingly important for complex missions, there remains a critical need for integrated approaches that can simultaneously handle task allocation and safe navigation in dynamic environments. This paper addresses the challenge of optimally allocating tasks and generating collision‐free trajectories for drone swarms operating in obstacle‐rich settings. Our proposed Swarm Allocation and Route Generation (SARG) framework integrates optimal task assignment with dynamically feasible trajectory planning, enabling efficient mission completion while ensuring safe navigation through complex 3D workspaces. Using quadrotors as our experimental platform, the framework incorporates both Drone‐to‐Obstacle and Drone‐to‐Drone collision avoidance algorithms, alongside a modified path planning algorithm that enhances simultaneous graph search efficiency. Our extensive experiments demonstrate that the SARG framework significantly improves performance over existing approaches. The SARG framework, while maintaining a 100% collision avoidance rate in dense environments, achieves a 21.6% reduction in the computation time of the simultaneous graph searching phase compared to conventional methods, contributing to overall system efficiency. These results establish SARG as a viable solution for real‐world autonomous drone swarm applications in complex, dynamic settings. Supporting Information, including animated simulations, are available at https://youtu.be/56oabPTUz4g .

The present study aims to explore experimental investigations and numerical simulations for temperature distributions at heat-affected zones within SA 387-Gr.11-Cl.2 steel during the submerged arc welding (SAW) process. Experimental endeavors entailed welding steel plates under controlled conditions, precisely measuring temperatures at key locations by thermocouples. A special program based on 3D Goldak’s double ellipsoidal model was developed in ANSYS Parametric Design Language for moving heat source calculations in the finite-element analysis (FEA). For welding an 8 mm thick plate with one pass, the suitable parameters were found to be 600 A current, 31 V voltage, and 10 mm·s ⁻¹ welding speed. The experimental cooling periods were found to be slower than predicted by FEA. When temperature distributions were compared between experimental and FEA results, an average variation of 1.88% at peak temperatures and 11.8% at completion time was observed. The results showed the temperature distribution at various time steps, illustrating the transient nature of the welding process. The results highlight the capacity of the FEA model to predict temperature profiles during SAW accurately, presenting a potent tool for optimizing welding parameters without extensive trial and error.

This research delves into the critical domain of high-temperature pressure vessel design, focusing on SA 387 Grade 11 Class 2 steel—an exceptional material known for its resistance to both corrosion and high temperatures. The study combines experimental investigations and numerical simulations to explore the intricate temperature distributions within this material during the Submerged Arc Welding (SAW) process. Experimental endeavors entailed welding SA387 steel plates under controlled conditions, precisely measuring temperatures at key locations. Concurrently, a numerical finite element analysis (FEA) model was developed, incorporating a 3D Goldak’s double ellipsoidal heat source model within ANSYS software. The research seamlessly integrates these two approaches, allowing for a comprehensive comparison of experimental and numerical temperature data. The results highlight the capacity of the FEA model to predict temperature profiles during SAW accurately, presenting a potent tool for optimizing welding parameters without extensive trial and error. Furthermore, this study advances our comprehension of welding processes involving high-temperature-resistant materials, offering valuable insights for industries requiring operations at elevated temperatures.