This research tackles the challenge of action fluctuation in articulated vehicle trajectory tracking control

aiming to enhance both accuracy and smoothness. It introduces a novel approach: a smooth tracking control methodology grounded in reinforcement learning (RL). Firstly

to improve the control accuracy

we incorporate trajectory preview information as input to both the policy and value networks and establish a predictive policy iteration framework. Then

to ensure control smoothness

we employ the LipsNet network to approximate the policy function

to realize the adaptive restriction of the Lipschitz constant of the policy network. Finally

coupled with distributional RL theory

we formulate an articulated vehicle trajectory tracking control method

named smooth distributional soft actor-critic (SDSAC)

focusing on achieving synergistic optimization of both control precision and action smoothness. The simulation results demonstrate that the proposed method can maintain good action smoothing ability under six different noise levels

and has strong noise robustness and high tracking accuracy. Compared with traditional value distribution reinforcement learning distributional soft actor-critic (DSAC)

SDSAC improves action smoothness by more than 5.8 times under high noise conditions. In addition

compared with model predictive control

SDSAC’s average single-step solution speed is improved by about 60 times

and it has higher online computing efficiency.