Optimal tracking controller for an autonomous wheeled mobile robot using fuzzy genetic algorithm

Abstract

This paper deals with development of a kinematics model, a trajectory tracking, and a controller of fuzzy-genetics algorithm for 2-DOF Wheeled Mobile Robot (WMR). The global inputs to the WMR are a reference position, P_r= (x_r,y_r,<i>&#952;</i>_r)^t and a reference velocity q_r=(v_r,&#969;_r) ^t, which are time variables. The global output of WMR is a current posture P_c= (x_c,y_c,<i>&#952;</i>_c)^t. The position of WMR is estimated by dead-reckoning algorithm. Dead-reckoning algorithm can determine present position of WMR in real time by adding up the increased position data to the previous one in sampling period. The tracking controller makes position error to be converged 0. In order to reduce position error, a compensation velocities q=(v,&#969;)^t on the track of trajectory is necessary. Therefore, a controller using fuzzy-genetic algorithm is proposed to give velocity compensation in this system. Input variables of two fuzzy logic controllers (FLCs) are position errors in every sampling time. The output values of FLCs are compensation velocities. Genetic algorithms (GAs) are implemented to adjust the output gain of fuzzy logic. The computer simulation is performed to get the result of trajectory tracking and to prove efficiency of proposed controller.