Design of Super Twisting Sliding Mode Controller for Trajectory Tracking Control of Autonomous Ground Vehicle System

Abstract

Autonomous Ground Vehicle (AGV) system research has getting a lot of attention in recent decades, within benefit of rapid advances in artificial intelligence technology and rising demand for traffic safety and efficiency. Autonomous Ground Vehicle system is an unstable, a highly nonlinear and time varying system. One of the main technologies in AGV research is trajectory tracking control, which is basic task of AGV. In this research work trajectory tracking control, Super Twisting Sliding Mode (STSMC) controller is designed for Autonomous Ground Vehicle (AGV) system based on Genetic algorithms (GA) and Particle Swarm optimization (PSO) technique. To compare the designed controller GA-based Fractional Order Proportional Integral Derivative (FOPID) controller was designed. Based on the bicycle model, the lateral dynamics model of the vehicle may established, which is further expressed in a state space form. The effectiveness and validation of the proposed method has been verified through different patterns of reference paths using MATLAB–Simulink software package. Also, the robustness of the system may conducted by adding disturbances due to friction of wheels during the vehicle motion. The obtained results of STSMC controllers are assumed to show the advantage and the good tracking performance of the technique in terms of minimizing trajectory tracking error, PSO based STSMC has less ( 0.0006sec) for orientation angle control and ( 0.0007sec) velocity control tracking error performance and the complement of the trajectory following over Fractional Order Proportional Integral Derivative (FOPID) controller. Finally PSO based STSMC controller was acceptable rather than GA based STSMC and GA and PSO based FOPID controller for trajectory tracking control of AGV system.