Abstract
In order to realize automatic steering controls of rice transplanters in paddy fields, an automatic steering control algorithm is essential. In this study, combining the fuzzy control with the proportional-integral-derivative (PID) control and the kinematics model, a compound fuzzy PID controller was proposed to adjust the real time data of the PID parameters for the automatic steering control. The Kubota SPU-68C rice transplanter was then modified with the new controller. Next, an automatic steering control experimental with the modified transplanter was carried out under two conditions of linear tracking and headland turning in verifying the automatic steering effect of the transplanter in different steering angle situations. The results showed that the deviation with the new controller and the modified transplanter was acceptable, with maximum deviation in linear tracking of 7.5 cm, the maximum headland turning a deviation of 11.5 cm, and the average a deviation of less than 5 cm. In conclusion, within the allowable deviation range of the field operation of the rice transplanter, the proposed algorithm successfully realized automatic steering controls of the transplanter under different steering angles.
Highlights
With the development of computers, automatic control, intelligent agricultural equipment technologies, and automatic navigation technology has received increased attention in the field of intelligent agricultural machinery [1,2]
Many different control methods have been developed for the automatic control system, including the pure pursuit model [5,6,7,8], the proportional-integral-derivative (PID) control [9,10], the fuzzy control [11,12,13], and the neural network [14,15,16,17]
When accurate mathematical models are not available, another option is to design a controller based on the PID control, the fuzzy control, and the neural network to realize automatic steering controls for agricultural machines
Summary
With the development of computers, automatic control, intelligent agricultural equipment technologies, and automatic navigation technology has received increased attention in the field of intelligent agricultural machinery [1,2]. The automatic steering control system in navigation is a key step in providing support for subsequent navigation control [3,4]. Many different control methods have been developed for the automatic control system, including the pure pursuit model [5,6,7,8], the proportional-integral-derivative (PID) control [9,10], the fuzzy control [11,12,13], and the neural network [14,15,16,17]. When accurate mathematical models are not available, another option is to design a controller based on the PID control, the fuzzy control, and the neural network to realize automatic steering controls for agricultural machines
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
