关键词: 六维力传感器, 下E膜, 混沌野草算法, 混沌序列, 扩展卡尔曼滤波器

Abstract:

The measurement accuracy of a sensor which worked on the environment of the dynamic load could be seriously affected by the pollution of noise signals and the EKF could not gain the optimal system noise matrix. A new EKF based on CIWO was proposed. The nonlinear state-space model was established based on the relationship between the deflection and the strain. The idea of the grass breeding was introduced to achieve the Gauss sampling of system interference matrix consisted of first six-order vibration mode informations and to produce the initial feasible solutions. After combining chaotic search technology with invasive weed optimization (IWO), the global search of the new algorithm was executed by the IWO. Then the chaotic sequences executed the local search to the individual which had the higher fitness value than average and guided the population approaching to the optimal solution. The search process can be avoided to fall into local optimum. Finally, the improved invasive weed optimization algorithm was introduced to optimize the system's noise matrix in EKF. The simulation results indicate that the new algorithm can enhance the measurement accuracy of six-axis force sensor effectively and maintain better robustness and real-time performance.
 

Key words: six-axis force sensor, lower E-type membrane, chaotic invasive weed optimization(CIWO), chaotic sequence, extended Kalman filtering(EKF)