Study on Deviation Control of Loader Working Device System

Abstract

In order to fulfill the fixed-height lifting function of the loader working device and reduce the deviation between the memory value of the controller and the actual value, this paper starts with the experimental curve, identifies the root cause of the deviation firstly, then separately analyses the influence of load, speed and target value on the deviation through the experimental data, eliminates the unnecessary factor load on the basis of the control accuracy, and then studies the influence of speed and target value on the deviation, and predicts the deviation by curve fitting and neural network based on the existing data. Finally, the prediction deviation is verified by experiments. The validation results of fixed speed show that the prediction results of neural network deviation meet the accuracy requirement of 94.29% and the prediction results of curve fitting deviation meet the accuracy requirement of 85.71%. These two methods of deviation prediction can basically control the deviation in the range of ±1°. And a further optimization method of threshold control is proposed. The validation results of variable speed show that the method of controlling deviation in advance is still applicable to variable speed. And according to the experimental data of variable speed, an optimization method of reducing speed deviation control is proposed.