关键词: 视觉信息, 随机路面激励模型, 视觉信息调节控制

Abstract: Traditional suspension systems can only mitigate vibrations after the vehicle receives road excitation, suffering from the drawbacks of an inability to proactively adapt to road condition changes in advance and time delays in the actuation of suspension actuators. To address these issues, a vision-based locally optimized fuzzy PID control strategy is proposed. Firstly, a visual recognition module, a road excitation module, and a communication platform are established. Subsequently, leveraging the information from road surface identification, transmission, and fusion, a vision-based locally optimized fuzzy PID control strategy is designed. Finally, through simulation modeling, the performance of different vehicle control strategies is validated under four distinct road excitation scenarios, including Class C and Class D road surfaces, as well as combinations of single and multiple speed bumps. The results demonstrate that, compared to passive suspension, PID control, and fuzzy PID control strategies, the proposed vision-based locally optimized fuzzy PID control strategy reduces the vertical acceleration RMS of the vehicle body by 32.4 %, 23.4 %, and 11.7 %, respectively, under Class C road excitation with a single speed bump; by 32.4 %, 21.9 %, and 8.1 %, respectively, under Class D road excitation with a single speed bump; by 32.2 %, 23.4 %, and 10.8 %, respectively, under Class C road excitation with multiple speed bumps; and by 31.0 %, 21.4 %, and 7.4 %, respectively, under Class D road excitation with multiple speed bumps.

Key words: visual information, stochastic pavement excitation model, visual information regulation and control