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

Meng Wang, Changhe Niu, Zifan Wang, Yongxin Jiang, Jianming Jian and Xiuying Tang ()
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Meng Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Changhe Niu: Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Zifan Wang: College of Engineering, China Agricultural University, Beijing 100083, China
Yongxin Jiang: Research Institute of Agricultural Mechanization, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Jianming Jian: College of Engineering, China Agricultural University, Beijing 100083, China
Xiuying Tang: College of Engineering, China Agricultural University, Beijing 100083, China

Agriculture, 2024, vol. 14, issue 6, 1-21

Abstract: 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.

Keywords: path tracking; rear-wheel steering model; MPC; preview; fuzzy control; PSO (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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