关键词: 六维力传感器, 下E型膜, 野草算法, 小生境技术, 扩展卡尔曼滤波器

Abstract: To reduce the influence of the noise for the measurement accuracy of the six-axis force sensor and solve the problem that the Extended Kalman filtering can’t gain the optimal system noise matrix, a new Extended Kalman Filtering（EKF） based on Niche Invasive Weed Optimization（NIWO） has been proposed. The nonlinear state-space model based on the relationship between the response of sinusoidal excitation force and the strain has been established. The idea of the grass breeding has been introduced to achieve the Gauss sampling of system interference matrix consisted of first six-order vibration mode information and to produce the initial feasible solutions. After combining niche technology?with Invasive Weed Optimization（IWO）, the global search of the new algorithm has been executed by the IWO. According to fitness value, the individual has been arranged in descending order. Multiple populations can be carved out to collaborate on the basis of the capacity of the niche. The search processing can be avoided to fall into local optimum. The improved invasive weed optimization algorithm is introduced to optimize the system’s noise matrix in EKF. The simulation results indicate that the new algorithm has better robustness and real-time performance. It can effectively enhance the measurement accuracy of six-axis force sensor.

Key words: six-axis force sensor, lower E-type membrane, invasive weed optimization, niche technology, extended Kalman filtering