Trajectory tracking of intelligent vehicle systems based on sampled data and adaptive dynamic programming

With the development of intelligent driving technology, the trajectory tracking of intelligent driving has been studied by many researchers. And the actual intelligent vehicle system is a hybrid system with sampled data. Consequently, a controller with sampled data based on the framework of adaptive dynamic programming (ADP) for trajectory tracking of the intelligent vehicle is proposed. The structure of this paper is as follows. First, the intelligent vehicle model is established in accordance with the frenet coordinate frame. Second, the closed-loop stability of the intelligent vehicle system with sampled data is analyzed by a new technique, and then the maximum sampling period can be obtained. Third, a controller composed of a forecasting module, a feedforward controller, and an actor-critic controller based on the framework of ADP is proposed. Last, the simulation result based on Carsim illustrates the validity of the control strategy.