Authors : Ralaivao Harinaivo Hajasoa; Mahera Salala Mandimby Hasina; Herinantenaina Edmond Fils

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/23k6za9z

Scribd : https://tinyurl.com/ywbdaudr

DOI : https://doi.org/10.38124/ijisrt/25sep1549

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Abstract : This paper presents a comparative study of two control strategies: fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) for autonomous guidance of a differential-drive mobile robot. The robot executes goal-seeking and reactive obstacle-avoidance tasks in a MATLAB Simulink environment using the Mobile Robotics Simulation Toolbox. Initially, a fuzzy logic controller with expert-defined IF–THEN rules generates linear and angular velocity commands while logging state and control data into a training matrix. These data are then used to train an ANFIS model, which is redeployed under identical simulation conditions. Both controllers are compared based on path-tracking accuracy, obstacle-avoidance robustness, and control-loop execution time. Simulation results indicate that the ANFIS controller reproduces the fuzzy logic decision boundaries with reduced computational latency, demonstrating the effectiveness of data-driven neuro-fuzzy models for real-time mobile robot control.

Keywords : Fuzzy Logic Control, ANFIS, Differential Drive, Target Reaching, Obstacle Avoiding.

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