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Model and Parameter Adaptive MPC Path Tracking Control Study of Rear-Wheel-Steering Agricultural Machinery

文献类型： 外文期刊

作者： Wang, Meng 1 ; Niu, Changhe 3 ; Wang, Zifan 1 ; Jiang, Yongxin 3 ; Jian, Jianming 1 ; Tang, Xiuying 1 ;

作者机构： 1.China Agr Univ, Coll Engn, Beijing 100083, Peoples R China

2.Xinjiang Inst Engn, Sch Mech & Elect Engn, Urumqi 830023, Peoples R China

3.Xinjiang Acad Agr Sci, Res Inst Agr Mechanizat, Urumqi 830091, Peoples R China

关键词： path tracking; rear-wheel steering model; MPC; preview; fuzzy control; PSO

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.3； 五年影响因子：3.5 ）

ISSN：

年卷期： 2024 年 14 卷 6 期

页码：

收录情况： SCI

摘要： To further enhance the precision and the adaptability of path tracking control, and considering that most of the research is focused on front-wheel steering, an adaptive parametric model predictive control (MPC) was proposed for rear-wheel-steering agricultural machinery. Firstly, the kinematic and dynamic models of rear-wheel-steering agricultural machinery were established. Secondly, the influence laws of curvature and velocity on the prediction horizon N-p, control horizon N-c, and preview value N-pre were obtained by simulating and analyzing the factors influencing the MPC tracking effect. The results revealed that raising N-pre can improve curve tracking performance. N-p was correlated negatively with the curvature change, whereas N-c and N-pre were positively connected. N-p, N-c, and N-pre were correlated positively with the velocity change. Then, the parameters for self-adaptation of N-p, N-c, and N-pre were accomplished via fuzzy control (FC), and particle swarm optimization (PSO) was utilized to optimize the three parameters to determine the optimal parameter combination. Finally, simulation and comparative analysis were conducted to assess the tracking effects of the manual tuning MPC, the FC_MPC, and the PSO_MPC under U-shaped and complex curve paths. The results indicated that there was no significant difference and all three methods achieved better tracking effects under no disturbance, with the mean absolute value of lateral error <= 0.18 cm, standard deviation <= 0.37 cm, maximum deviation of U-shaped path <2.38 cm, and maximum deviation of complex curve path <3.15 cm. The mean absolute value of heading error was <= 0.0096 rad, the standard deviation was <= 0.0091 rad, and the maximum deviation was <0.0325 rad, indicating that manual tuning can find optimal parameters, but with high uncertainty and low efficiency. However, FC_MPC and PSO_MPC have better adaptability and tracking performance compared to the manual tuning MPC with fixed horizons under variable-speed disturbance and are more able to meet the actual needs of agricultural machinery operations.

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