Estimation of Tractor Driving Wheel Slip Rate Based on Improved Adaptive Kalman Filtering
Journal Title: 河南科技大学学报（自然科学版） - Year 2017, Vol 38, Issue 4
Abstract
To solve the problem of low accuracy and poor real-time in estimating tractor driving wheel slip rate, a tractor slip rate measurement system model was established. The traditional Sage-Husa adaptive Kalman filtering algorithm was improved by calculation step simplification and noise estimation simplification. The MATLAB program of the improved algorithm was written and comparied with other algorithms. The simulation results show that the improved variable structure Sage-Husa adaptive data fusion algorithm can estimate the statistical property of noise and achieve the real-time and accurate estimation of tractor driving wheel slip rate without prior error statistical experiment. The sliding rate estimated by the improved adaptive Kalman filtering algorithm is robust under white noise jamming. The average error of the sliding rate estimated by the improved adaptive Kalman filtering algorithm is about 16% of that estimated by the median filtering method.
Authors and Affiliations
Zhili ZHOU, Yao LIU, Liyou XU
Keywords
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