Performance analysis of different meta-heuristic methods in unmanned vehicles

Nowadays, unmanned vehicles are actively used in various fields, rangingfrom defense industries to logistics, agriculture, and search-and-rescue operations. Inparticular, multi-body unmanned vehicles stand out due to their high payloadcapacity, enhanced maneuverability, and improved stability. However, the control ofthese vehicles, which involve multiple bodies and propulsion systems, presentssignificant engineering challenges due to their complex dynamic structureandvarying external factors. This thesis aims to conduct a dynamic analysis of multi-body unmannedvehicles and develop effective control methods. First, existing studies onsuchsystems are reviewed, and the modeling and control approaches used in the literatureare evaluated. Then, different control algorithms are applied to analyze the stability, mobility, and overall performance of the system. In addition to classical control methods, modern approaches such as adaptive control and artificial intelligence-based algorithms are tested and compared in terms of their effectiveness. In addition, artificial intelligence methods were used to save energy and time by makingpathplanning for unmanned vehicles. The results obtained indicate that artificial intelligence and advanced algorithms provide highly accurate and stable control formulti-body unmanned vehicles. Keywords: Unmanned vehicles, multi-body systems, dynamic modeling, control algorithms, stability analysis