Autonomous Vehicle Trajectory Tracking Control Based on Deep Deterministic Policy Gradient Algorithm and Crayfish Optimization Algorithm

The widespread application of unmanned vehicles in logistics distribution and special transportation has made improving trajectory tracking accuracy and dynamic adaptability critical for operational efficiency. This article proposes a biologically inspired combination control strategy based on the Deep Deterministic Policy Gradient (DDPG) algorithm, enhanced by the Crayfish Optimization Algorithm (COA) to address limitations in generalization and dynamic adaptability. The proposed DDPG-COA controller embodies a symmetrical structure: DDPG acts as the primary controller for global trajectory tracking, while COA serves as a compensatory regulator, dynamically optimizing actions through a disturbance observation mechanism. This symmetrical balance between learning-based control (DDPG) and bio-inspired optimization (COA) ensures robust performance in complex scenarios. Experiments on symmetrical trajectories demonstrated significant improvements, with the average tracking errors reduced by 56.3 percent, 71.6 percent, and 74.6 percent, respectively. The results highlight how symmetry in control architecture and trajectory design synergistically enhances precision and adaptability for unmanned systems.