Practical implementation of a type-2 fuzzy logic controller for steering a service robot

Abstract

Service robots are designed to assist humans in various tasks and often rely on wheeled locomotion for navigation. Effective robot movement requires a robust control system to regulate steering and ensure precise maneuvering toward locations. However, a common challenge in service robot navigation is the lack of precision in steering control. To address this issue, this study implements and evaluates a steering control system for wheeled service robots using a type-2 fuzzy logic controller (T2-FLC). The proposed T2-FLC system incorporates two input variables: error (difference between the setpoint determined by the light detection and ranging sensor and the steering encoder reading) and de-error (difference between the current and previous error values). Subsequently, these inputs are converted into three, five, or seven membership functions (MFs). Comparative simulation analysis revealed that the T2-FLC with seven MFs outperformed that with alternative MF configurations and a conventional type-1 FLC and achieved a minimal steady-state error of 0.0118. Real-time experiments further validated these findings, with the seven-MF T2-FLC producing a steady-state error of only 3.6 during a 90° setpoint test. In obstacle navigation trials, a T2-FLC-equipped robot navigated to target destinations in 32.49 s in stationary obstacle scenarios and within 41.78 s in dynamic obstacle environments. These findings confirm that the T2-FLC significantly enhances steering performance, making it viable for controlling service robot navigation.