自适应Kalman滤波修复六维力传感器下E膜模型误差

为减小动载环境下，噪声信号对六维力传感器测量精度的影响，同时解决因传感器的简化模型误差较大，导致标准Kalman滤波无法获取最优估计的问题，提出一种双因子自适应Kalman滤波算法。算法根据正弦激励力响应和应变之间的关系，建立了下E型膜有色噪声增广状态模型。在标准Kalman滤波的基础上，分析了两种模型误差对滤波效果的影响，采用实时调整状态预测在滤波估计中权重的策略，给出了自适应Kalman滤波准则及递推公式。基于正交性原理和最小二乘法准则，利用三段函数模型构造了双重自适应因子。仿真实例表明，与标准Kalman滤波与强跟踪滤波相比，所提算法具有更好的估计精度和稳定性，能够有效地控制模型误差的影响，从而提高六维力传感器的测量精度。

Model error restoration for lower E-type membrane of six-axis force sensor based on adaptive Kalman filtering

To reduce the influence of the noise on the measurement accuracy of the six-axis force sensor and solve the problem that the standard Kalman filter can not gain the optimal estimation because of the state-space model error of the sensor, a new adaptive Kalman filtering with two adaptive factors was proposed. The augmented state-space model of colored noise for lower E-type membrane based on the relationship between the response of sinusoidal excitation force and the strain was established. Based on the principle of standard Kalman filter, the impact of model errors on the filter estimate results were analyzed. The technology of dynamically adjusting the weight of state prediction in the filter estimation was introduced. The adaptive Kalman filter estimation principle and the recursion formula were presented. Finally, the dual adaptive factors were constructed through the model of three-section function on the basis of orthogonality principle and least square method. The simulation results indicate that comparing with the strong tracking filter and standard Kalman filter, the proposed algorithm has better estimate accuracy and stability. It can effectively enhance the measurement accuracy of six-axis force sensor and control the influence of model errors.