Hierarchical Cooperative Control Strategy of Unmanned HMCVT Tractor Considering Trajectory Tracking Performance and Economy

XU Liyou, TAO Yuan, ZHANG Junjiang, WEN Changkai, WANG Dongqing, LIU Mengnan, YAN Xianghai

Abstract

Most of the unmanned tractors in the process of trajectory tracking focus on the tracking performance, ignoring the operational energy consumption resulting in poor economy. Aiming at the above problems, a layered collaborative control strategy that considered economy and trajectory tracking performance was proposed. Firstly, the trajectory tracking system was established by model predictive control algorithm with longitudinal deviation and transverse deviation as the target and acceleration and front wheel angular speed as the constraints, and secondly, the binary regulating economic control strategy based on external parameter optimization was established by taking the ratio of engine fuel consumption rate and hydro-mechanical CVT transmission efficiency as the optimization target. On this basis, the trajectory tracking system and economic control strategy were integrated to form a hierarchical cooperative control strategy with the predicted speed of the tractor at the next moment and the current plow resistance of the tractor as the transfer variables. Pure tracking and one-dimensional economic control strategy were used as the comparison strategy, and the cooperative control strategy was simulated based on the Matlab simulation platform, and the effectiveness of the cooperative control strategy was verified by the hardware-in-the-loop test platform. The results showed that compared with the comparison strategy, the layered cooperative control strategy effectively reduced the trajectory tracking deviation of the unmanned tractor and improved the tractor’s economy, the speed variance was reduced by 36.7% , the longitudinal tracking deviation was reduced by 89.8% , the lateral tracking deviation was reduced by 91.7% , and the tractor fuel consumption was reduced by 11.8% .

 

Keywords: unmanned tractor;hydro-mechanical continuously variable transmission, model predictive control algorithm, trajectory tracking, cooperative control strategy

 

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References


ZHANG Man, JI Yuhan, LI Shichao, et al. Research progress of agricultural machinery navigation technology [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(4) : 1-18. (in Chinese)

KIM D H,CHOI С H,KIM Y J. Analysis of driving performance evaluation for an unmanned tractor [J]. IFAC-PapersOnLine, 2018, 51(17): 227 -231.

SHEN X, CHE H, YAO Z, et al. Real-world emission characteristics of full-volatility organics originating from nonroad agricultural machinery during agricultural activities [J] . Environmental Science & Technology, 2023, 57(28) ; 10308 - 10318.

LIU Y, LI Y, YUAN Z, et al. Identification of two main origins of intermediate-volatility organic compound emissions from vehicles in China through two-phase simultaneous characterization [J] . Environmental Pollution, 2021 , 281 ; 117020.

XIA Y, SUN D. Characteristic analysis on a new hydro-mechanical continuously variable transmission system [ J] . Mechanism and Machine Theory, 2018, 126: 457 -467.

CHENG Z, LU Z, QIAN J. A new non-geometric transmission parameter optimization design method for HMCVT based on improved GA and maximum transmission efficiency [J ]. Computers and Electronics in Agriculture, 2019, 167: 105034.

ZHU Qingyuan, CHENG Jiaqi, CHEN Xuanwei, et al. Review on path tracking control of unmanned articulated steering vehicles[ J]. Transactions of the Chinese Society for Agricultural Machinery, 2024, 55( 1 ) : 1-21. (in Chinese)

SHEN Yue, ZHANG Yafei, LIU Hui, et al. Research review of agricultural equipment automatic control technology [ J ]. Transactions of the Chinese Society for Agricultural Machinery, 2023, 54(8) ; 1 - 18. (in Chinese)

YIN C, WANG S, GAO J, et al. Trajectory tracking for agricultural tractor based on improved fuzzy sliding mode control[ J ]. Plos One, 2023, 18(4): e0283961.

ZHOU Weiqi, ZHAO Yihan, LIU Qingchao, et al. Lateral control strategy of vehicle path tracking based on improved LQR J . Journal of Huazhong University of Science and Technology ( Natural Science Edition) , 2024, 52 ( 3 ) : 135 -141. (in Chinese)

ZHANG Y, PEI W , ZHANG Q, et al. A generalized Hamilton robust control scheme of trajectory tracking for intelligent vehicles [ J ]. Sensors, 2023 , 23 (15): 6975.

SHI P, LI L, N1 X, et al. Intelligent vehicle path tracking control based on improved MPC and hybrid PID[ J ]. IEEE Access, 2022, 10: 94133 -94144.

ZHAO Shuen, WANG Sheng, LENG Yao. Multi-objective explicit model predictive control for intelligent vehicle trajectory tracking [J], Automotive Engineering, 2024, 46(5) ; 784 -794, 815. (in Chinese)

WANG Hongbo, WANG Chunyang, ZHAO Linfeng, et al. Variable-parameter MPC multi-objective control for intelligent vehicle path tracking based on reinforcement learning [ J]. China Journal of Highway and Transport, 2024, 37(3) ; 157 - 169. (in Chinese)

ZHANG Mingzhu, WANG Quansheng, BAI Dongyang, et al. Speed changing law of hydro-mechanical CVT based on maximum efficiency of tractors [ J ]. Transactions of the CSAE, 2016, 32(21) ; 74 -78. (in Chinese)

HU Haifeng, SHI Lixin, ZHANG Shiqing. Simulation and experiment of hydro-mechanical CVT control strategy for tractors [ J]. Control Engineering of China, 2017, 24( 11): 2290 -2295. (in Chinese)


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