Simulation Study of Deep Belief Network-Based Rice Transplanter Navigation Deviation Pattern Identification and Adaptive Control

The navigation field of agricultural machinery has entered the intelligent stage, but the navigation control performance of paddy field agricultural machinery represented by rice transplanters is not stable in complex environments. Therefore, this study proposes a method to identify navigation deviation patterns based on Deep Belief Network (DBN) and designs an adaptive preview distance control method based on a driver preview model for each deviation pattern. Among them, the deviation pattern identification method is a two-stage algorithm. First, determine whether the current navigation status is abnormal. Then, the classification was refined for different abnormal states. The adaptive control method is divided into two levels. The main regulator calculates the dynamic preview distance according to the current state variable; the sub-regulator calculates the preview distance adjustment value according to the abnormal state degree. In the performance test of the identification method, all the models show excellent stability and accuracy, and the identification speed of the algorithm meets the high frequency of the rice transplanter navigation system. In the performance test of the control algorithm, compared with the static preview distance, the adaptive preview distance control method proposed in this study can effectively suppress the disturbance deviation of the rice transplanter navigation.