Trajectory tracking by automated guided vehicle using GA optimized sliding mode control

Abstract

The current state-of-Art trajectory tracking control for four wheel differential driven vehicle is found to be unreliable in certain cases. The vehicle motion needs to be controlled using optimization algorithms in order to follow the desired trajectory satisfactorily. Sliding mode controller (SMC) is employed for such non-linear systems to make trajectory tracking robust taking into account all surrounding uncertainties. The algorithm obtains an intermediate sliding surface for the vehicle such that the error between actual and desired trajectory is minimum. In this paper, Genetic Algorithm has been used to tune SMC gain parameters. The mathematical model of the vehicle has been developed in the paper for generating desired control actions. The conventional PID control is also applied separately for trajectory tracking by the vehicle. A comparative analysis between two controllers is presented in this paper. The results obtained show the tracking efficiency of SMC even in the presence of disturbances. © 2016 IEEE.