Target-tracking control method for autonomous vehicles based on hyperbolic-tangent line-of-sight guidance and odometry error compensation.

The target-tracking accuracy of autonomous vehicles is closely related to that of onboard sensors. Methods such as image processing and base station positioning are susceptible to various types of interference in real-world scenarios, resulting in sensor data errors or even losses that ultimately affect the tracking accuracy of autonomous vehicles. This study proposes a target-tracking control method that relies solely on wheel odometry to address this issue. This method incorporates an extended state observer to compensate for the cumulative errors generated by the odometry mechanism, effectively enhancing the robustness and accuracy of the system in complex environments. In addition, a hyperbolic-tangent line-of-sight guidance strategy based on a partition-switching mechanism is designed to improve the dynamic response capability of an autonomous vehicle. This strategy nonlinearly adjusts the tracking error to generate the desired heading angle and velocity, ensuring that the target path tracking is rapid and smooth. First, we establish a mathematical model of an autonomous vehicle and combine the hyperbolic-tangent line-of-sight guidance strategy with a noise-resistant active disturbance rejection controller to achieve high-precision target tracking in dynamic environments. Second, an extended state observer is employed to perform real-time observations and compensate for unknown disturbances during localization, significantly reducing the impact of cumulative errors. Finally, the effectiveness of the proposed method is validated using numerical simulations and real vehicle experiments. The experimental results demonstrate that, compared with the ET-Fuzzy-MPC method, the proposed method lowered the average position tracking error by 45.39% under complex road conditions. In practical curved-path tests, the vehicle's tracking error remained stable to within 0.192 m, representing a significant improvement in the target tracking accuracy and dynamic response performance.