【摘要】 针对含间隙锥齿轮系统动力学特性转迁及其控制问题，提出了基于胞元映射理论的参数解域结构和基于径向基函数神经网络的控制方法。采用集中质量法建立了7自由度弧齿锥齿轮动力学模型，基于胞元映射理论构建了频率和负载参数平面，采用伪不动点延续追踪算法求解了系统的分岔、齿面冲击、脱啮、齿背接触和动载特性转迁规律，分析发现频率和齿面冲击是影响周期分岔的主要因素，随负载增大其脱啮、冲击减弱，动载系数增大。针对平面中系统混沌现象，设计了参数反馈控制器、基于Poincaré截面欧氏距离构造适应度函数，用自适应引力搜索算法对控制器参数进行优化，从而实现了系统混沌、拟周期和周期运动向周期轨道有效控制。更多还原

【Abstract】 A parametric solution domain structure based on the cell mapping theory and a control method based on a radial basis function neural network were proposed to solve the dynamic characteristic transition and the control problem of a bevel gear system with backlash. A dynamics model of 7-degree-of-freedom spiral bevel gear was established by using the concentrated mass method. Then, the frequency and load parameter plane was constructed based on the cell mapping theory, and the pseudo-fixed point continuous tracking algorithm was used to solve the transition rule of bifurcation, tooth surface impact, tooth no-meshing, tooth back contact and dynamic load characteristics of the straight bevel gear system. It was found that frequency and tooth impact are the main factors affecting the periodic bifurcation. With the increase of load, the tooth no-meshing and impact weaken, and the dynamic load coefficient increases. For the chaotic phenomena of the system in the plane, a parametric feedback controller was designed, and the fitness function was constructed based on Poincaré cross section Euclidean distance. The adaptive gravity search algorithm was used to optimize the controller parameters, so as to realize the effective control of chaos, quasi-period and periodic motions to periodic orbit.更多还原