Prediction model of pitch angle of greenhouse electric tractors based on time series analysis

Abstract

The pitch angle of greenhouse tractors changes when ope

rating on rough soil pavement. As a result, the feedback signal

lags behind the tractor motion attitude signal, thereby affecting

the real-time control of tilling depth. In this study, a pitch angle

prediction model of greenhouse electric tractor was proposed ba

sed on extended Kalman filter (EKF) and time series analysis to

improve the dynamic response speed of tilling depth regulation

by providing predictive information for advance control. EKF was

used to track the tilling depth of greenhouse electric tractor in

real time, and an auto-regressive moving average model (ARMA)

was established for the obtained time series data. ARMA (2, 1)

was designed as the pitch angle prediction model of greenhouse

electric tractors by constructing a simulation model. Inertia mea

surement unit (IMU) of tractor was used to construct the extended

Kalman estimation model of the pitch angle. Actual vehicle tests

were carried out under different working conditions. Results show

that the estimated values obtained under two operating condi

tions have a high correlation with the measured values, with co

rrelation coefficients(R) of 0.9504 and 0.9734, root mean square

error (RMSE) of 0.2355 and 0.2173, and maximum absolute error

(MAE) of 0.1929 and 0.1703, respectively. And ,the MAE and the

RMSE of the predicted and measured values of ARMA (2,1) model

approximately have the same value under the two conditions, with

with the R of 0.9665 and 0.9755, the RMSE of 0.2002 and 0.1812,

and the MAE of 0.1578 and 0.1387, respectively. The effectiveness

of ARMA (2, 1) as the pitch angle estimation and prediction mo

del of greenhouse electric tractors is verified. This study provides

theoretical reference for designing the control law of tilling depth

stability in subsequent greenhouse operation.