Dereje, Shiferaw (PhD)Nigusu, Teshome2020-03-072023-11-282020-03-072023-11-282018-05http://etd.aau.edu.et/handle/12345678/20967In autonomous trajectory tracking navigation of mobile robots in the static environment is a source of problems. Because it is not possible to model all the possible conditions, the key point in the robot control is to design a system that is adaptable to different conditions and robust in static environments. The subject of this thesis primarily addresses the trajectory tracking control of a differential drive mobile robot based on Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. ANFIS has two layers like input fuzzy layer and the following neural network layer. The hybrid system combines the advantages of fuzzy logic, which deal with explicit knowledge that can be explained and understood, then also neural network, which deal with implicit knowledge, which can be acquired by learning. The kinematic modeling is developed for non-holonomic wheeled mobile robot systems. A learning algorithm based on neural network technique is developed to tune the parameters of fuzzy membership functions, which smooth the trajectory tracking with minimal error for the given path. Using the developed ANFIS controller, the mobile robots can be able to track the trajectory, and reach the target successfully in cluttered environments. The control objective has been to make the mobile robot actuator traces desired trajectory using ANFIS based controller. This has been done through the simulation of the robot model using the software MATLAB/Simulink version R2017a for a Pioneer 3-DX mobile robot.en-USAdaptive Neuro - Fuzzy Inference System (ANFIS)Mobile RobotControlTrajectory TrackingThesis