Backstepping Sliding Mode Control for Hydraulic System in Farmland Leveller Based on Hybrid Extended State Observer

被引:0
|
作者
Wei, Xinhua [1 ,2 ]
Wang, Qingzhuang [1 ]
Ji, Xin [1 ]
Liu, Chengliang [3 ]
Wang, Anzhe [1 ]
机构
[1] School of Agricultural Engineering, Jiangsu University, Zhenjiang,212013, China
[2] Key Laboratory for Theory and Technology of Intelligent Agricultural Machinery and Equipment, Zhenjiang,212013, China
[3] School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai,200240, China
关键词
Backstepping;
D O I
10.6041/j.issn.1000-1298.2024.07.004
中图分类号
学科分类号
摘要
In order to address the issues of low control accuracy and poor anti-interference capability of farmland leveller's hydraulic systems when operating on complex terrains, a backstepping sliding mode controller based on a hybrid extended state observer ( HESO) was designed. The HESO estimated the system's unknown states and total disturbances based on output feedback signals and compensates for disturbances in the feedforward channel. The backstepping sliding mode controller, designed based on the fast reaching law, outputed continuous and smooth control quantities, enhancing the system's robustness and overcoming the issues of system nonlinearity and parameter uncertainty. The stability of the proposed observer and controller was proven by using Lyapunov stability theory, resulting in a conclusion of uniformly bounded stability of errors. The effectiveness and superiority of the control algorithm proposed were verified through joint simulations with AMESim and Matlab/Simulink, as well as field experiments. Single flattening experiments on two wavy terrains with relatively complex geomorphology and three traversal experiments on two similar terrains showed that using the proposed control method, the average absolute error, maximum absolute error, and standard deviation of the absolute error of the elevation after flattening compared with the pre-flattening elevation were 0. 053 m, 0. 146 m, 0. 037 m, and 0. 02 m, 0. 041 m, 0. 011 m, respectively. These values were reduced by 36. 35% , 28. 32% , 31. 37% , and 62. 6% , 50% , 51. 83% respectively, compared with that of the PID algorithm. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.
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页码:37 / 46
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