## 1 Introduction  
An AGV is a portable robot that follows marked lines or wires on the ground or uses radio waves, cameras, magnets or lasers for navigation. Computer-controlled and wheel-based AGVs are load carriers that travel across the floor of a facility without an onboard operator or driver. AGVs are also used to repeat transport duties in other areas such as warehouses, container terminals and external (underground) transport systems [1, 2]. Studies on AGV in the literature have generally focused on AGV design, trajectory control and power consumption analysis. In the light of the information in the literature, factors such as the number of pick-up and drop-off points, route, traffic management, flow path layout, vehicle requirements and battery management are important in the design of AGV. [PERSON] et al. [3] worked on a low-cost AGV design in their studies and created a frame for an AGV that can carry a 200 kg load and performed its structural tests. A different design of a high-payload AGV with Mecanum wheels was created by [PERSON] et al. [4]. For this AGV carrying at least 740 kg, engine power analysis and route-tracking performance during motion were examined. [PERSON] [5] designed a six-wheel magnetic-navigation-basedAGV and observed successful route tracking with fast response and strong stability with its fuzzy-logic controller. There are many patents and papers in the literature for many AGVs produced for different purposes [6; 7; 8; 9; 10]. Another criterion that stands out regarding the performance of AGVs is the tracking performance of the given routes. AGVs can move along different routes according to the task and the environment in which the task takes place. Since they work together with other factors in an environment, it is very important for them to follow this determined route, both in terms of efficiency and security. For this reason, many studies in the literature have focused on modeling and trajectory control of AGVs. Since AGVs are a type of mobile robot, studies on modeling are generally based on mobile robots. These robot types differ according to the number of wheels and wheel designs. The kinematic and dynamic modeling of a mobile robot with four mechanical wheels was investigated by [PERSON] and [PERSON] [11] and [PERSON] and [PERSON] [12] under different conditions. Another type of four-wheeled mobile robots are robots that can be driven and rotated independently on four wheels, and their kinematic and dynamic modeling has been studied by [PERSON] and [PERSON] [13] and [PERSON] et al. [14]. Three-wheeled mobile robots are also used for AGVs. In these mobile robots, while the rear two wheels are fixed, the front wheel can have a non-holonomic structure, and there are types in which each wheel can be controlled separately. The dynamic and kinematic models of three-wheeled mobile robots have been studied in detail in the literature [15; 16; 17]. When the studies on trajectory control, which is one of the performance criteria for robots for which mathematical models are obtained, are examined, it is seen that many control techniques and approaches are used to achieve successful tracking performance. For two-wheeled AGVs, a model predictive control-based control strategy was developed by [PERSON] et al. [18], and an improved reaching-law-based sliding mode controller was developed by [PERSON] et al. [19]. For the control of a three-wheeled AGV, [PERSON] et al. [20] developed a sliding mode variable structure control theory based on a new control rule with the Lyapunov method. Other studies on the control of a three-wheeled AGV include a new control strategy based on the Udwadia-Kalaba approach [21], time-varying state feedback control [22], adaptive learning-based model predictive control strategy [23] and the linear parameter varying control strategy [24]. For the control of four-wheeled AGVs, many techniques have been similarly applied.