Adaptive Sliding Mode Path Tracking Control of Unmanned Rice Transplanter

Jinyang Li (), Zhijian Shang, Runfeng Li and Bingbo Cui
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Jinyang Li: College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhijian Shang: College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Runfeng Li: College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Bingbo Cui: College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 8, 1-14

Abstract: To decrease the impact of uncertainty disturbance such as sideslip from the field environment on the path tracking control accuracy of an unmanned rice transplanter, a path tracking method for an autonomous rice transplanter based on an adaptive sliding mode variable structure control was proposed. A radial basis function (RBF) neural network, which can precisely approximate arbitrary nonlinear function, was used for parameter auto-tuning on-line. The sliding surface was built by a combination of parameter auto-tuning and the power approach law, and thereafter an adaptive sliding controller was designed. Based on theoretical and simulation analysis, the performance of the proposed method was evaluated by field tests. After the appropriate hardware modification, the high-speed transplanter FLW 2ZG-6DM was adapted as a test platform in this study. The contribution of this study is providing an adaptive sliding mode path tracking control strategy in the face of the uncertainty influenced by the changeable slippery paddy soil environment in the actual operation process of the unmanned transplanter. The experimental results demonstrated that: compared to traditional sliding control methods, the maximum lateral deviation was degraded from 17.5 cm to 9.3 cm and the average of absolute lateral deviation was degraded from 9.1 cm to 3.2 cm. The maximum heading deviation was dropped from 46.7° to 3.1°, and the average absolute heading deviation from 10.7° to 1.3°. The proposed control method not only alleviated the system chattering caused by uncertain terms and environmental interference but also improved the path tracking performance of the autonomous rice transplanter. The results show that the designed control system provided good stability and reliability under the actual rice field conditions.

Keywords: autonomous rice transplanter; path tracking control; RBF neural network; automatic steering; navigation system (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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