Wang Shuqing, Jin Huichao, Wang Yu, Huo Junyi, Liu Xudong
Department of Electrical Engineering, Shijiazhuang Institute of Railway Technology, Shijiazhuang, 050041, China.
Sci Rep. 2025 Jul 1;15(1):22340. doi: 10.1038/s41598-025-08651-6.
Active vibration control in flexible structures remains a critical challenge in engineering applications. This study proposes an optimization framework for piezoelectric sensor and actuator configurations in cantilever beams using a genetic algorithm. An objective function based on controllability and observability criteria was formulated, integrating modal strain and natural frequency analyses. A small-habitat genetic algorithm was employed to determine optimal sensor/actuator positions, validated through frequency- and time-domain simulations. Comparative experiments with three alternative control methods demonstrated improved vibration suppression, achieving amplitude rejection rates as low as 1.2% under random and sinusoidal excitations. The method was further extended to a vehicle suspension model, where the proposed system achieved near-zero suspension dynamic travel under step inputs, outperforming the other three control methods in terms of vibration suppression effectiveness. Results highlight the framework's adaptability for enhancing structural resilience in aerospace, automotive, and robotic systems, providing a systematic approach for optimizing smart material configurations in vibration-sensitive applications.
柔性结构中的主动振动控制在工程应用中仍然是一个关键挑战。本研究提出了一种使用遗传算法对悬臂梁中的压电传感器和致动器配置进行优化的框架。基于可控性和可观测性标准制定了一个目标函数，该函数整合了模态应变和固有频率分析。采用小生境遗传算法来确定最佳传感器/致动器位置，并通过频域和时域模拟进行了验证。与三种替代控制方法的对比实验表明，振动抑制效果得到了改善，在随机和正弦激励下实现了低至1.2%的振幅抑制率。该方法进一步扩展到车辆悬架模型，在阶跃输入下，所提出的系统实现了近乎零的悬架动态行程，在振动抑制效果方面优于其他三种控制方法。结果突出了该框架在增强航空航天、汽车和机器人系统结构弹性方面的适应性，为在振动敏感应用中优化智能材料配置提供了一种系统方法。
