关键词: 六维力传感器, 下E型膜, 模型误差, 自适应Kalman滤波, 双重自适应因子

Abstract:

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.

Key words: Six-axis force sensor, lower E-type membrane, model error, adaptive Kalman filtering, multiple adaptive factor