Trajectory Tracking And Point Stabilization Control For Autonomous Vehicle

Due to the nonholonomic constraint, it has important theoretical value and practical significance to study the autonomous vehicle. The vehicle motion control problem is attracting more and more scholars and experts to concern. The problems of autonomous vehicle motion control main contain trajectory tracking control, path keeping control, and point stabilization control. Based on the wheeled vehicle, this paper focuses on the autonomous vehicle trajectory tracking and point stabilization control issues, which are applied to the actual control of autonomous vehicles.Based on the kinematic model of autonomous vehicles, this paper research on issues related to the motion control, the accurate pose information is need, so the indoor ultrasonic location system is established and corrected, which provides accurate pose feedback for indoor autonomous vehicle. In the control, the hardware structure of autonomous vehicle limit its movement speed, this paper design the PID speed controller of the motor, and test the limits of its velocity and angular velocity.By using the Backstepping method, trajectory tracking controller for autonomous vehicles is designed. Simulation results show that the controller is feasible, and compared to Lyapunov controller, the controller is proved efficient. Finally, combined with input constraints and saturation phenomenon, the controller is effectively used in the actual control. By the experiments of the autonomous vehicle tracking the circular trajectory, sine curve, Li Saru graphics, we find the control works well.Stepwise method is applied to the control of autonomous vehicles point stabilization control problem, which step control autonomous vehicles each state convergence, and ultimately realize the entire state convergence. The step point stabilization controller of this paper, successfully applied to the control of autonomous vehicles point stabilization control problem, and works well. Through model Transform, a new controller is design which control the two state of new model convergence firstly, then control the residual state convergence. The simulation and Experiment results show that the controller is feasible and practical.