神经网络阻尼比模型及工业机器人导纳控制

在工业机器人打磨过程中,环境刚度随未知环境的变化,将对力控制精度产生不利的影响,针对环境刚度变化的问题,该文提出一种基于神经网络阻尼比模型的自适应导纳控制方法。在导纳控制设计中,根据力误差与系统阻尼比之间的机理关系,设计激励函数,构造神经网络阻尼比模型;通过该模型使阻尼比在线调整,适应末端环境的刚度变化,实现力到位置自适应转换的导纳控制。与常规导纳控制进行仿真比较,结果表明所提出的力控制策略力误差更小,响应速度更快,能适应变刚度的未知打磨环境。

结构和构件损伤判别及阻尼比变化规律研究

基于动力特性的建筑结构健康监测具有操作便捷及高效反馈的特点,是建筑结构健康监测最常用的手段之一。动力特性中的自振频率等内容已被广泛研究和应用,而阻尼比这一重要参数的关注却比较少。研究认为国内外规范设计中将阻尼比值规定为常数的方法,忽略了阻尼比值在弹塑性阶段的变化规律。总结了黏滞阻尼模型、库伦阻尼模型和复阻尼模型这3种常见的阻尼比模型,并着重分析了应用最广泛的黏滞阻尼模型,介绍了损伤状态的判别方法。在此基础上,整理总结了在不同损伤状态下结构和构件阻尼比变化的公式和趋势的探索研究。最后,将黏滞阻尼模型和不同损伤状态下阻尼比变化规律进行了总结,建议将阻尼比和自振频率综合在一起作为损伤指标以更全面评估损伤状态。

Effect analysis of friction and damping on anti-backlash gear based on dynamics model with time-varying mesh stiffness

A nonlinear model of anti-backlash gear with time-varying friction and mesh stiffness was proposed for the further study on dynamic characteristics of anti-backlash gear. In order to improve the model precision, applied force analysis was completed in detail, and single or double tooth meshing states of two gear pairs at any timing were determined according to the meshing characteristic of anti-backlash gear. The influences of friction and variations of damping ratio on dynamic transmission error were analyzed finally by numerical calculation and the results show that anti-backlash gear can increase the composite mesh stiffness comparing with the mesh stiffness of the normal gear pair. At the pitch points where the frictions change their signs, additional impulsive effects are observed. The width of impulsive in the same value of center frequency is wider than that without friction, and the amplitude is lower. When gear pairs mesh in and out, damping can reduce the vibration and impact.