Abstract
Rotary burying by tractor-hitched rotary tillers is a common practice in southern China for treating rice stubbles. Currently, it is difficult to maintain stable tillage depths due to surface unevenness and the residual stubbles in the field, which leads to unstable tillage quality and nonuniform crop growth in later stages. In this study, an RTK-GNSS was used to measure the real-time height and roll angle of the tractor, and a variable-gain single-neuron PID control algorithm was designed to adjust the coefficients (KP, KI, and KD) and gain K in real-time according to the control effects. An on-board computer sent the angles of the upper swing arm u(t) to an STM32 microcontroller through a CAN bus. Compared with the current angle of the upper swing arm, the microcontroller controlled an electronic-control proportional hydraulic system, so that the height of the rotary tiller could be adjusted to follow the field undulations in real-time. Field experiments showed that when the operation speed of the tractor-rotary tiller system was about 0.61 m/s, the variable-gain single-neuron PID algorithm could effectively improve the stability of the working depth and the stubbles’ burying rate. Compared with a conventional PID controller, the stability coefficient and the stubbles’ burying rate were improved by 5.85% and 4.38%, respectively, and compared with a single-neuron PID controller, the stability coefficient and the stubbles’ burying rate were improved by 4.37% and 3.49%, respectively. This work controlled the working depth of the rotary tiller following the changes in the field surface in real-time and improved the stubbles’ burying rate, which is suitable for the unmanned operation of the rotary burying of stubbles in the future.
Highlights
The multi-cropping rice planting area in the middle and lower reaches of the Yangtze River is one of the main rice planting areas in China
In order to verify the performance of the variable-gain single-neuron proportional integrative derivative (PID), this study introduced a conventional incremental PID and a common single-neuron PID for simulation comparison
The upper and lower limits of K of the variable-gain single-neuron PID were determined as KA = 0.09 and KB = 2.15 by the trial and error method
Summary
Compared with the hydraulic mechanical system, the electronic hydraulic control system improves the traction efficiency of tractor operation, and the tillage depths under different field conditions are more stable [17,18,19]. Shafaei et al [28] developed an electronic hydraulic control system for a tractor (MF-399) based on fuzzy PID, which could control the working depth of the tools in real-time. Han et al [29] proposed a hybrid traction position control strategy based on a fuzzy controller; despite that, the complex field environment increased the depth error and the response time of the control system, and the stability of the control system was improved by the control strategy.
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