Prediction Method of Tractor Ploughing Resistance Based on Multi-source Heterogeneous Sensor Data
Abstract
In order to realize the precise control of ploughing resistance and improve the traction efficiency of tractor in ploughing operation, a prediction method of ploughing resistance was proposed. A plowing resistance perception model based on the upper pull rod force was proposed, and the experimental verification was carried out. In view of the problem that the measurement results were unstable by only sensing the ploughing resistance through the upper pull rod force in the actual field operation, a ploughing operation parameter test platform was built, and the multi-source heterogeneous sensor data based on tillage depth, upper pull rod force, vehicle speed and wheel speed were obtained and the prediction samples were constructed. The wavelet threshold denoising (WTD) and sparrow search algorithm (SSA) were introduced into the least squares support vector machine (LSSVM), and the combined prediction model of ploughing resistance based on WTD-SSA-LSSVM was established and the model performance was verified. The results showed that the model prediction method had higher accuracy than that of the upper pull rod force. In addition, different prediction methods were compared. The R2, MAE, RMSE and MAPE of the test set obtained by the combined prediction model method were 0.97, 118.1N, 151.4N and 2.2%, respectively. Compared with prediction models of LSSVM and SSA-LSSVM, R2 was increased by 8.9% and 5.4%, respectively. MAE was decreased by 49.7% and 42.2%, respectively. RMSE was decreased by 46.7% and 39.1%, respectively. MAPE was decreased by 56.8% and 48.8%, respectively. Therefore, the method proposed had better prediction performance and was more suitable for the prediction of tractor plough resistance.
Keywords: tractor;ploughing resistance;model prediction;least squares support vector machine;multi-source heterogeneous sensor data
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