# Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM
###### Abstract
[11] constructed a fuzzy neural network to predict the traction resistance of tractor tillage and optimized the structure of the fuzzy neural network through a density-based noise application spatial clustering algorithm, and the average relative error of the predicted resistance was 4.36%. Although the above resistance models achieved good prediction accuracy, they all predicted the special point of insertion depth, ignored the characteristics of resistance change in time sequence, and could not achieve a prediction for the trend of resistance change. For the characteristics of bucket insertion resistance, which is a timeseries and nonlinear, this study chooses to use the long short-term memory neural network (LSTM) as the basic model. LSTM is a classical method of deep learning [12; 13], which addresses the problem of gradient disappearance or gradient explosion of recurrent neural networks [14; 15]. LSTM can ascertain future data changes by historical timeseries data features, and has been widely used in many fields. Meanwhile, the introduction of the dropout layer in LSTM can effectively prevent overfitting in the training process [16]. However, LSTM is highly sensitive to the number of hidden layer units, the initial learning rate, and the dropout probability, and different parameters will have a large impact on the prediction performance. The best number of hidden layer units, initial learning rate, and dropout probability are not the same for different training sets and samples [17]. To address the shortcomings of LSTM, this paper combines particle swarm optimization (PSO) to optimize the above parameters. The PSO algorithm has the characteristics of fast searching speed, high efficiency, and easy convergence [18; 19]. The model, by combining LSTM with PSO, has been applied to many fields. [PERSON] et al. [20] used a variational modal decomposition method to decompose the propagation loss of electromagnetic waves, and combined PSO and LSTM to achieve the prediction of electromagnetic wave propagation loss in ocean tropospheric ducts. [PERSON] et al. [21] used LSTM as the basic model, combined with empirical modal decomposition and PSO, to achieve prediction for electric power fluctuations. The model improved the basis for improving the generator's fluctuation compensation capability. [PERSON] et al. [22] developed a PSO-LSTM model to estimate the state-of-charge of lithium-ion batteries, and the error was within 5%. [PERSON] et al. [23] developed a glacier runoff prediction model by PSO-LSTM, and they used the Muzati River basin as the study object and achieved good prediction accuracy. [PERSON] et al. [24] analyzed and determined the input layer using the random forest method and the Pearson correlation coefficient method. The PSO-LSTM model was used to predict the ammonia concentration in a piggery and the \(R^{2}\) achieved 0.9487. [PERSON] et al. [25] constructed a PSO-LSTM model to predict short-term passenger flow in subway stations, and the prediction accuracy of PSO-LSTM is higher compared to LSTM. At the same time, the reasonable selection of characteristic data that can represent the trend of resistance changes assumes a high priority. [PERSON] et al. [26] proposed a method to calculate the resistance of an excavator by analyzing the relationship between the hydraulic cylinder, excavator bucket, and resistance. The resistance obtained by this method is closer to the actual value and can effectively reflect the dynamic process of the actual resistance during the excavation operation as well as the transient change in the actual resistance and the impact state. This provides a new idea for predicting the insertion resistance of wheel loaders. In response to the above problems and discoveries, this study proposes a PSO-LSTM model to predict the magnitude and change trend of bucket insertion resistance. Three sets of experiments with different insertion depths of 600 mm, 800 mm, and 1000 mm were conducted in each of two typical piles, gravel and sand.