中文摘要：

为减小噪声信号对六维力传感器测量精度的影响,同时解决因主振型信息缺失导致扩展Kalman滤波器难以获得最优系统估计的问题,提出一种基于小生境野草算法优化的扩展卡尔曼滤波（NIWO-EKF）算法。算法根据正弦激励力响应与应变之间的关系,构建六维力传感器下E型膜非线性系统模型。将系统干扰矩阵与控制矩阵视为一个整体,引入野草繁殖思想,以前6阶主振型信息构成的综合矩阵为均值,进行高斯采样,产生初始化的可行解。将小生境技术与野草算法相融合,利用野草算法进行全局搜索,根据适应度的大小对个体进行降序排列,按照小生境容量划分出多个种群协同合作,避免搜索过程陷入局部最优,提高算法的寻优精度和收敛速度。采用改进野草算法对EKF中的系统干扰控制矩阵进行优化处理。仿真实例表明,优化后的扩展卡尔曼滤波器能有效地提高六维力传感器的测量精度,具有很好的鲁棒性和稳定性。

英文摘要：

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.