Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network

Abstract

To study the influence of speed factors on the stability of a tractor automatic navigation system, combined with the neural network control theory, the authors proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using a back propagation neural network to establish a two-wheel tractor-path dynamics model and a straight-line path tracking deviation model. The overall system simulation was carried out using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed that when the tractor was tracked with the automatic control of a linear path under the condition of variable speed, the maximum deviation of the lateral position deviation was 12.7 cm, and the average absolute deviation was kept within 4.88 cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the conditions of constant speed and variable speed, using the joint sliding mode control method designed by the authors, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.