Trajectory Tracking of Autonomous Vehicle: A Differential Flatness Approach With Disturbance-Observer-Based Control

In this article, the problem of trajectory tracking control is studied for autonomous vehicle with consideration of the nonlinearity and coupling characteristics. To achieve accurately trajectory tracking in longitudinal and lateral motions simultaneously, a flatness disturbance-observer-based control scheme is developed to address the kinematic coupling issue of the vehicle, which can avoid extra modeling errors introduced by linearization. The main idea is that, derived from a suitable selection of flat outputs, the complex kinodynamic vehicle model is transformed into three fully-actuated subsystems with same structure, which will provide convenience for controller design. Then, in order to compensate the lumped disturbances caused by modeling and transformation, a disturbance-observer-based control strategy is designed via backstepping. Trajectory tracking control is accomplished through longitudinal, lateral and yaw motions simultaneously. The stability of the closed-loop dynamics is guaranteed in the framework of Lyapunov and input-to-state stability theories. Finally, the CarSim simulation tests demonstrate that the proposed integrated control method can successfully promote the trajectory tracking performance in different manoeuvres.

Automated summary

In this article, a control scheme based on flatness disturbance-observer is developed to address the trajectory tracking control problem for autonomous vehicles considering nonlinearity and coupling characteristics. The proposed method transforms the complex kinodynamic vehicle model into three fully-actuated subsystems for convenient controller design. A disturbance-observer-based control strategy is designed to compensate for disturbances caused by modeling and transformation, and trajectory tracking is achieved in longitudinal, lateral, and yaw motions simultaneously. The proposed control method is validated through CarSim simulation tests, showing improved trajectory tracking performance in various maneuvers.