Using Type-1 and Type-2 Fuzzy Logic Controllers for the Trajectory Tracking Task of a Wheeled Robot: A Comparison Study

The robotic path-tracking task is of interest to researchers because it offers the potential to develop an efficient navigation system for robots. Fuzzy logic is successfully used in many control systems, especially in robotic tasks, due to its ability to model the uncertainties and vagueness of the physical world. In this paper, the application of type-1 and type-2 fuzzy logic controllers for trajectory tracking of differential drive robots has been investigated. Initially, a comprehensive review of related work is provided, followed by a detailed description of the differential-drive robot, including its kinematic and dynamic models. Both type-1 and type-2 fuzzy controllers are implemented to evaluate their performance in tracking complex, challenging trajectories. Simulation results demonstrate the effectiveness of each fuzzy controller, with a focus on comparative analysis. All comparisons are conducted under strictly identical conditions to ensure a fair and unbiased evaluation of both controllers. A comparison study highlights differences in performance metrics across scenarios, revealing that the type-2 fuzzy logic controller outperforms the type-1 controller in improving trajectory tracking accuracy. Quantitative performance indicators, including root-mean-square errors (RMSEs) for distance and orientation, as well as transient response times, are employed for comparison. Specifically, the type-2 fuzzy controller reduced the average tracking error by more than 75% and the angular error by over 80% across different trajectories, while also decreasing the response time by up to 80% compared to the type-1 fuzzy controller.