Wheel loader shovel loading operation tests showed that when the tire slips, it not only causes waste of the engine power, but also increases the tire wear. In this paper, the regression prediction model is combined with the electro-hydraulic proportional control technology. For the first time, the method of adjusting the posture of the working device is proposed to realize the skid-proof shovel control strategy of the wheel loader. In this control strategy: (1) The electro-hydraulic proportional control technology applied to this wheel loader is introduced. (2) The load spectrum of the wheel loader is analyzed during the shovel loading operation. Moreover, combined with the drive shaft torque, pilot pressure and boom cylinder displacement, the particle swarm optimization-support vector machine (PSO-SVM) algorithm is used to complete the construction of the regression prediction model of the boom cylinder displacement. (3) A controller is designed based on the fractional-order PID control algorithm. The skid-proof control strategy is simulated and verified by constructing the joint simulation model. The corresponding program is prepared in the hydraulic system controller and the effectiveness of the control strategy and algorithm are verified by the wheel loader shovel loading operation.

In this paper, the wheel loader with electrohydraulic proportional control technology is used as the carrier. For the first time, the dual-angle sensor is used for intelligent operation, which allows the wheel loader working device to be precisely controlled. First, the theoretical analysis of the electrohydraulic proportional control technology on the wheel loader studied in this paper is carried out. Next, according to the feedback of the boom and bucket angle sensor signals, the electrohydraulic proportional control technology is used to initially realize the boom memory and bucket automatic levelling function of the wheel loader working device. Finally, the data acquisition equipment is connected to provide experimental verification, although the test results did not achieve precise control of the working device. After analysis, the detected problems were solved by constructing a neural network algorithm model, which successfully realizes the intelligent and precise operation of the wheel loader, reducing unnecessary energy loss.