The Loading Control Strategy of the Mobile Dynamometer Vehicle Based on Neural Network PID

To solve the problems of low loading precision, slow response speed, and poor adaptive ability of a mobile dynamometer in a tractor traction test, a PID control strategy based on a radial basis function neural network with self-learning and adaptive ability is proposed. The mathematical model of the loading system is established, the algorithm of adaptive control is described, and the loading control method is simulated with MATLAB software. The system, which uses the NN-PID (neural network PID) control strategy, is used to test a YTO-MF554 tractor. Then, the proposed control strategy is validated. Results show that when the traction increases from 0 to 10 kN, the response time of the test system is 1.5 s, the average traction force in the stability range is 10.13 kN, and the maximum relative error of traction force is 2.2%. This control strategy can improve the response speed and steady-state accuracy and enhance the adaptive ability of the mobile dynamometer vehicle loading system. This study provides a reference for designing the adaptive controller of the mobile dynamometer vehicle loading system.