# Research on the Prediction of Insertion Resistance of Wheel Loader Based on PSO-LSTM
###### Abstract  
Insertion resistance is the resistance caused by a pile to a wheel loader when the latter inserts into the pile. It is significant to clarify the insertion resistance to avoid wheel slippage, increase additional energy consumption, and protect the wheel loader during the insertion process. To address the problem that current methods cannot accurately obtain the insertion resistance magnitude and insertion resistance variation trend, we propose a composite model based on the particle swarm optimization (PSO) algorithm and the long short-term memory (LSTM) neural network. Firstly, the Pearson correlation coefficient method is used to test the parameters related to insertion resistance. Following this, the hyperparameters in the LSTM are optimized by PSO. Finally, different proportions of training sets are set in PSO-LSTM and compared with LSTM. The experimental data are selected from gravel sample groups and sand sample groups consisting of insertion depths of 600 mm, 800 mm, and 1000 mm. The results show that PSO-LSTM has higher prediction accuracy, better robustness, stability, and generalization ability compared with LSTM. In PSO-LSTM, when the proportion of the training set is 80%, the average relative errors are 2.28%, 1.57%, and 1.53% for the gravel sample group and 1.14%, 0.71%, and 0.60% for the sand sample group. wheel loader; insertion resistance; PSO; LSTM +
Footnote †: journal: Applied  
the soil mechanics method is that the calculation process is complicated. In recent years, a bucket-resistance-prediction method based on the agent model has gradually become mainstream, and the advantage of this is that it relies on the establishment of an agent model between the mechanical motion of the wheel loader and the resistance, and then obtains the resistance value through the change in mechanical motion, which largely reduces the error caused by the manual selection of coefficients. [PERSON] et al. [7] developed a static model of bucket working resistance and a kinematic model of the working trajectory of a wheel loader, from which the key measurement parameters of working resistance were obtained. Based on the static and kinematic models, the corresponding working trajectories and working resistance were fitted based on the indirect measurement parameters. The analysis results showed that the minimum deviation of the resistance was 0.05%, the maximum deviation was 19.59%, and the average deviation was 7.07%. [PERSON] et al. [8] analyzed the forces during the shoveling operation of the wheel loader, established the dynamics and kinematics equations of the whole wheel-loader system, and then established a dynamic model for bucket resistance estimation and verified that the resistance error was less than 1%. Although agent models have many advantages, their accuracy depends on dynamic or kinematic models. These models are the result of theoretical analysis, which differs from the actual operating conditions of the bucket and does not reflect the trend of resistance. This problem has been effectively solved by the advent of artificial neural networks (ANN), which are non-parametric models that learn from training data to obtain predicted values of output variables. There have been many successful applications of ANN in predicting insertion resistance. [PERSON] et al. [9] constructed a neural network with parameters such as the diameter of the pile, the insertion depth, the insertion energy, and the soil properties around the pile as the input variables and the insertion resistance as the output variable, and verified the feasibility of using neural networks to predict insertion resistance. [PERSON] et al. [10] used a BP neural network to predict the resistance to the curved blade and selected the cutting angle, forward turning angle, and cutting speed of the blade as input variables, and the accuracy of the prediction results for the resistance reached 94%. [PERSON] et al.