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作 者:陈旺达[1] 徐志玲[1] 厉志飞[2] CHEN Wang-da1 , XU Zhi-ling1 , LI Zhi-fei2(1.China Jiliang University, Hangzhou, Zhejiang 310018, China; 2. Hangzhou Institute of Calibration and Testing for Quality and Technical Supervision, Hangzhou, Zhejiang 310019, Chin)
机构地区:[1]中国计量大学,浙江杭州310018 [2]杭州市质量技术监督检测院,浙江杭州310019
出 处:《计量学报》2018年第3期326-331,共6页Acta Metrologica Sinica
摘 要:在游标类量具检定过程中,当检定装置的驱动系统的动态工作台处于低速运动状态时,由于与滚珠丝杠之间存在着摩擦和齿隙非线性的误差问题,导致无法将游标类量具精确地移动到检定规程所要求的检测点。为解决该误差问题,运用LuGre摩擦模型和自适应律周期性递归小波神经网络进行补偿,并根据李诺夫稳定性进行分析,保证了闭环系统的有界性和收敛性。仿真实验验证了位置跟踪性能的改善,将控制补偿方案在检定装置的驱动系统中进行了实验论证,结果检定装置正反行程的定位精度分别提高了47.6%和49.7%。In the verification process of vernier measuring tools, when the dynamic table of the drive system in the verification device is at a low speed, the errors of the friction and backlash nonlinearity are exist between the dynamic work station and the ball screw, so the vernier measuring tools cannot be accurately moved to the test point required by the verification procedure. In order to solve the problem of the errors, the LuGre friction model and adaptive periodic recursive wavelet neural network are used to compensate the friction and backlash nonlinearity. Based on the Liroff stability analysis, the boundedness and convergence of the closed-loop system are guaranteed. The simulation results show that the performance of position tracking is improved, and the control compensation scheme is demonstrated in the driving system of the verification device, the accuracy of positive is improved by 47.6% in positive movement and 49. 7% in negative movement.
关 键 词:计量学 游标类量具 驱动系统 非线性补偿 递归小波神经网络
分 类 号:TB921[一般工业技术—计量学]
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