In autonomous trajectory tracking navigation of mobile robots in the static environment is a sourcernof problems. Because it is not possible to model all the possible conditions, the key point in thernrobot control is to design a system that is adaptable to different conditions and robust in staticrnenvironments.rnThe subject of this thesis primarily addresses the trajectory tracking control of a differential drivernmobile robot based on Adaptive Neuro-Fuzzy Inference System (ANFIS) controller. ANFIS hasrntwo layers like input fuzzy layer and the following neural network layer. The hybrid systemrncombines the advantages of fuzzy logic, which deal with explicit knowledge that can be explainedrnand understood, then also neural network, which deal with implicit knowledge, which can bernacquired by learning. The kinematic modeling is developed for non-holonomic wheeled mobilernrobot systems. A learning algorithm based on neural network technique is developed to tune thernparameters of fuzzy membership functions, which smooth the trajectory tracking with minimalrnerror for the given path. Using the developed ANFIS controller, the mobile robots can be able torntrack the trajectory, and reach the target successfully in cluttered environments. The controlrnobjective has been to make the mobile robot actuator traces desired trajectory using ANFIS basedrncontroller. This has been done through the simulation of the robot model using the softwarernMATLAB/Simulink version R2017a for a Pioneer 3-DX mobile robot.