Algorithm and verification for estimating tractor driving wheel slip rate

Abstract: Calculation process strongly amplifies the relative error of input signal, which makes it difficult to measure the sliding rate. The key to obtain the accurate value of the sliding rate lies in the real-time and accurate measurement of the tractor speed and the theoretical speed of the driving wheel. Multi sensor information fusion algorithm based on Kalman filtering can effectively improve the measurement accuracy. Due to the development of controller area network, sliding rate measuring node can share the information of other sensors on the bus based on ISO11783 protocol, which provides a convenient condition for realizing multi sensor fusion. However, the measurement noise variance of the sensor signals of the tractor is unpredictable, which is the problem that the algorithm must solve. Aimed to this problem, the adaptive data fusion algorithm with noise observer is proposed in this paper. Multi sensor signals from wheel speed sensor, angular acceleration sensor, vehicle body accelerometer and global positioning system are integrated by this algorithm, and at the same time, the noise variance of the sensor signal is calculated online, so as to accurately estimate the tractor driving wheel's sliding rate online without the prior noise measurement signal variances. The simulation results show that the sliding rate estimated by the proposed algorithm is almost coincident with the theoretical value. The average error of the sliding rate estimated by adaptive data fusion algorithm is about 1/10 of that by the median filtering method, and 1/5 of that by the Kalman filtering method. The algorithm has good robustness. The noise observer of the algorithm can estimate the white noise variance of the measured signals in real time, and there is no significant difference between the average estimated variance of steady state and the Kalman data fusion algorithm with exact prior error. The algorithm has the mechanism of the fusion according to the weight of the signal covariance, and the distortion signal can be modified by other related signals in the fusion process, so the algorithm has good robustness. Under the special condition that driven wheel speed signal is disturbed by colored random noise, the information fusion mechanism of the algorithm can compensate for the errors caused by the disturbance of colored noise. Experiments show that the estimated value of driving wheel sliding rate is very close to its true value, and for the estimated value, the mean error is 0.012 and the maximum error is 0.027. Under the condition of stable working of tractor, the mean value of the noise variances of the measured signals is within 5%. So the mean value can be used to replace the real-time estimation, so as to improve the real-time performance of the system. This algorithm can estimate the noise variance of the sensor signal online, which provides the technical basis for the measuring nodes on the bus network to share the sensor signals from other sensors on the bus. The precise sliding rate by the algorithm can reflect the real-time traction efficiency of the tractor, and provide the basic parameter for the control system of the tractor.