Abstract
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.
Highlights
The loading process of mobile dynamometer vehicles presents many uncertainties in the tractor traction test
This study provides a reference for designing the adaptive controller of the mobile dynamometer vehicle loading system
A considerable number of theories and practices have shown that the application of self-adaptable control technology to the test system will improve the efficiency and accuracy of the test system [1,2,3,4]
Summary
The loading process of mobile dynamometer vehicles presents many uncertainties in the tractor traction test. Wang et al proposed the fuzzy adaptive PID control for the nonlinear and time-variant mobile dynamometer of an automobile [5] He et al expounded the output prediction of complex nonlinear systems based on the radial basis function (RBF) neural network and obtained well-predicted results [6]. The RBF neural network PID control strategy is used for the load control of the mobile dynamometer vehicle. The output load for power wagon random loading system can reproduce the tractive load for tested tractor reasonably This control strategy can improve loading precision and response speed and enhance the self-adaptive ability of the control system. This method can provide a reference for the investigation of mobile dynamometer vehicle loading control
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