| During the last two decades, the wheeled mobile robots have been increasingly used in industrial and service robotics to perform such tasks as moving material between work stations. They are also found in many areas such as industrial, medical, environmental and even domestic machines. This thesis studies the trajectory tracking of the wheeled differential drive mobile robot.;In this work, a trajectory tracking control scheme is proposed to solve trajectory tracking problems in nonlinear dynamic models of the mobile robot. The wavelet neural network (WNN) and the particle swarm optimization (PSO) algorithm are used to design the best motion controller for the wheeled mobile robot. The performance is measured based on the variable values of the mean square error (MSE).;The work is divided into two sections; in the first section, the best structure is selected from wavelet neural network by changing the number of neurons (N = 10, 20, 30, 40, 50,100, 150, 200) in the hidden layer. The results are compared based on the variable values of MSE; the structure which contains the smallest value of MSE is the best choice. In the second section, the best structure of the fixed number of neurons, found in section one, is used for the different types of wavelet functions such as Morlet, Polywog, Shannon, and Mexican hat.;The simulation of the mobile robot is specially designed to test and implement the proposed control schemes by using MATLAB Version 7.12.0.635(R2011a). |
