分层优化PF在六维力传感器下E型膜中的应用

Application of Hierarchical Optimal Particle Filtering in Lower E-type Membrane of Six-axis Force Sensor

针对动载环境下,噪声污染导致六维力传感器测量精度急剧下降的问题,提出一种具有分层优化步骤的改进粒子滤波算法。以双E型弹性体六维力传感器下E型膜为研究对象,根据正弦激励力响应和应变的关系,建立非线性系统模型。在粒子滤波的框架下,将样本集按权值的蜕化程度分层,引入野草繁殖算法,将最新的观测信息融入高权值子集。基于Thompson-Taylor算法,通过聚合重采样将高、低权值粒子随机组合,产生中权值粒子集。将优化后的粒子滤波算法在六维力传感器动态测试系统中进行仿真研究,结果表明,该算法能以更小的估计误差贴近真实后验概率密度,在保持实时性的同时,有效地提高六维力传感器的测量精度。

The measurement accuracy of the sensor which works on the environment of the dynamic load can be seriously affected by the pollution of noise signal. A new improved particle filtering which owns hierarchical optimal steps is proposed. This algorithm takes the rectangular thin plate of dual-E elastic body six-axis force sensor as the research object. The nonlinear state-space model based on the relationship between the response of sinusoidal excitation force and the strain is established. According to degenerate level,the sample sets can be divided into two parts. Based on the weeds breeding algorithm,the new measurement can be transferred to high likelihood region. Based on the Thompson-Taylor algorithm,the new particles set produced by random combinations of particles are achieved through polymerization resample of transferred particles. Simulation results indicate that the new algorithm can adjoin the real posterior probability density with smaller estimated error. It can effectively enhance the measurement accuracy of six-axis force sensor and maintain the real-time performance.