基于改进哈里斯鹰算法的齿轮传动误差研究
The Study of Gear Transmission Error Based on the Improved Harris Hawk Algorithm

针对三维立体编织装备在运行时编织参数误差较大的问题，文章研究了一种基于改进哈里斯鹰优化算法PID的参数整定控制同步电机连接齿轮传动的方法。首先采用Tent混沌映射和小孔成像反向学习方法保证初始种群的多样化及增加精英个体数量，提升算法收敛性能；其次，在能量线性递减机制中引入动态自适应权重的非线性表达，提升算法全局搜索及局部开发行为的平衡能力；最后将该方法分别与HHO和PSO进行比较。仿真结果表明：齿轮1的初始转速误差分别降低1.9%、2.9%，负载突变转矩误差分别降低0.75%、4.75%。
To address the issue of large errors in weaving parameters when operating three-dimensional weaving equipment, the article studies a parameter tuning control method based on an improved Harris Hawk Optimization (HHO) algorithm with PID proposed for the synchronous motor connected to a gear drive system. Firstly, the Tent chaotic mapping and small hole imaging reverse learning methods are employed to ensure the diversity of the initial population and increase the number of elite individuals, enhancing the algorithm’s convergence performance. Secondly, a dynamic adaptive weight nonlinear expression is introduced into the energy linear decay mechanism to improve the balance between the global search and local exploitation capabilities of the algorithm. Finally, the proposed method is compared with HHO and PSO. Simulation results show that the initial speed error of gear 1 is reduced by 1.9% and 2.9%, respectively, and the load torque fluctuation error is reduced by 0.75% and 4.75%, respectively.