智能收获机器人侧滑及滞后性控制策略研究

doi:10.12398/j.issn.2096-7217.2022.02.005

智能化农业装备学报（中英文） ›› 2022, Vol. 3 ›› Issue (2): 37-44.DOI: 10.12398/j.issn.2096-7217.2022.02.005

智能收获机器人侧滑及滞后性控制策略研究

王焯轩^1,2, 王立辉^1,2, 许宁徽^1,2, 李勇^1,2, 石佳晨^1,2

1. 东南大学仪器科学与工程学院，江苏南京，210096；
2. 微惯性仪器与先进导航技术教育部重点实验室，江苏南京，210096

出版日期:2022-11-15 发布日期:2022-11-15
通讯作者: 王立辉
作者简介:王焯轩

Research on the control strategy of sideslip and hysteresis of intelligent harvesting robot

Wang Zhuoxuan^1,2, Wang Lihui^1,2, Xu Ninghui^1,2, Li Yong^1,2, Shi Jiachen^1,2

1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China;
2. Key Laboratory of Micro-inertial Instrument and Advanced Navigation Technology, Ministry of Education, Nanjing 210096, China

Online:2022-11-15 Published:2022-11-15
Contact: Wang Lihui
About author:Wang Zhuoxuan, Postgraduate, research interests: intelligent agricultural robot navigation, path planning and control. E-mail：wangzhuoxuan83@163.com
Supported by:
Primary Research & Development Plan of Jiangsu Province (BE2022389); National Natural Science Foundation of China (51875260); Jiangsu Province Agricultural Science and Technology Independent Innovation Fund Project （CX(22)3091）

摘要/Abstract

摘要： 针对智能重载收获机器人在田间自动作业时容易发生侧滑及控制系统滞后性特点，提出一种能够补偿侧滑和校正滞后性的机器人路径追踪控制策略。建立收获机器人的运动学模型，根据上一时刻和当前时刻的位姿信息估算等效侧滑角，补偿期望转向角；根据当前时刻的速度和位姿信息预测未来时刻的位姿信息，校正当前时刻的横向偏差和航向偏差，优化由路径追踪算法计算的期望转向角，以克服田间作业侧滑问题，校正控制系统的滞后性、非线性对路径追踪精度的影响。通过试验验证了在0.6 m/s和1.0 m/s的行驶速度下该算法的有效性，收获机最大横向误差分别为8.3 cm和13.9 cm，标准偏差分别为3.01 cm和4.09 cm。结果表明，所提出的补偿侧滑和校正滞后性的纯追踪策略能有效降低横向误差，实现准确的路径追踪控制。

关键词: 智能重载收获机器人, 纯追踪算法, 非线性, 滞后性, 侧滑

Abstract: Aiming at the characteristic that the heavy-load harvesters would easily slip and sideslip and its nonlinear large-lag control system, a method of compensating the sideslip and relieving the hysteresis in path tracking for heavy-load harvesters was proposed in this paper. After establishing a kinematic model, the equivalent sideslipping angle was estimated according to the previous and the present state of the harvester, to compensate for the expected wheel steering angle. The featured state of the harvester can be predicted according to the present state and speed of the harvester to adjust the present lateral error and heading angle error to optimize the expected wheel steering angle calculated by the path tracking algorithm, which can improve the accuracy and stability when the heavy-load harvester is working. The validity of the proposed algorithm was verified by experiments at the speeds of 0.6 m/s and 1.0 m/s, respectively. The maximum lateral errors of the harvester were 8.3 cm and 13.9 cm, respectively, and the standard deviations were 3.01 cm and 4.09 cm respectively. The results show that the strategy proposed in this paper can effectively reduce the lateral error and achieve accurate path tracking control.

Key words: intelligent heavy-load harvester, pure pursuit algorithm, nonlinear, control delay, sideslip

中图分类号:

王焯轩, 王立辉, 许宁徽, 李勇, 石佳晨. 智能收获机器人侧滑及滞后性控制策略研究[J]. 智能化农业装备学报（中英文）, 2022, 3(2): 37-44.

Wang Zhuoxuan, Wang Lihui, Xu Ninghui, Li Yong, Shi Jiachen. Research on the control strategy of sideslip and hysteresis of intelligent harvesting robot[J]. Journal of Intelligent Agricultural Mechanization, 2022, 3(2): 37-44.

参考文献

［1］Dong S, Yuan Z H, Gu C, et al. Research on intelligent agricultural machinery control platform based on multi-discipline technology integration ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2018(18): 14-19. (in Chinese)
［2］Fang G H, Chang F. Application of fuzzy control in hydraulic steering by-wire system ［J］. Machinery Design & Manufacture, 2015(1): 141-143. (in Chinese)
［3］Kralev J, Mitov A, Slavov T, et al. Robust mu-controller for electro-hydraulic steering system ［C］// IEEE Eurocon 2019-18th International Conference on Smart Technologies. IEEE, 2019.
［4］Song J J, Yang X H, Li Z, et al. Shift energy-saving tracking control research based on heavy-duty hybrid electric vehicle ［J］. Computer Simulation, 2018, 35(9): 167-171. (in Chinese)
［5］Shi G B, Zhou Q, Wang S, et al. High robust control strategy for electro-hydraulic hybrid steering system in unmanned mode ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2019, 50(12): 395-402.
［6］Gong J H. Study on control system and obtain ideal steering ratio for tractor hydraulic steering by-wire system ［D］. Nanjing: Nanjing Agricultural University, 2017. (in Chinese)
［7］Du H, Wang Z, Wang Y, et al. Adaptive robust control of multi-axle vehicle electro-hydraulic power steering system with uncertain tire steering resistance moment ［J］. IEEE Access, 2018.
［8］Li S H, Yang S P. Research status of hysteretic nonlinear models ［J］. Journal of Dynamics and Control, 2006(1): 10-17. (in Chinese)
［9］Xie S L, Xie Z D, Tian S P. The learning control algorithm for nonlinear discrete system with delay ［J］. Acta Automatica Sinica, 2001(1): 18-23. (in Chinese)
［10］Benioudakis M, Burnetas A, Ioannou G. Lead-time quotations in unobservable make-to-order systems with strategic customers: Risk aversion, load control and profit maximization ［J］. China Measurement & Testing Technology, 2017, 43(5): 127-131.
［11］Wei S, Li S C, Zhang M, et al. Automatic navigation path search and turning control of agricultural machinery based on GNSS ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(Z1): 70-77. (in Chinese)
［12］Lü A T, Song Z H, Mao E R. Optimized control method for tractor automatic steering ［J］. Transactions of the Chinese Society of Agricultural Engineering, 2006, 22(8): 116-119. (in Chinese)
［13］Gao D X, Rui W. A non-delay optimal disturbance rejection control approach for nonlinear systems with external disturbances and control delay ［J］. Procedia Engineering, 2011(15): 454-458.
［14］Wei R M, Jin S J. Trajectory tracking control of mobile robot under influence of slipping and sideslipping ［J］. Transducer and Microsystem Technologies, 2019, 38(3): 117-119, 130. (in Chinese)
［15］Yoo S J. Approximation-based adaptive control for a class of mobile robots with unknown skidding and slipping ［J］. International Journal of Control Automation & Systems, 2012, 10(4): 703-710.
［16］Guo L, Ge P S, Yang X L, et al. Intelligent vehicle trajectory tracking based on neural networks sliding mode control ［C］// Proceedings 2014 International Conference on Informative and Cybernetics for Computational Social Systems (ICCSS). IEEE, 2014.
［17］Cui M Y, Sun L H, Li Y F, et al. Adaptive tracking control of wheeled mobile robots in presence of longitudinal slipping ［J］. Control and Decision, 2013, 28(5): 664-670. (in Chinese)
［18］Hou Z G, Zou A M, Cheng L, et al. Adaptive control of an electrically driven nonholonomic mobile robot via backstepping and Fuzzy approach ［J］. IEEE Transactions on Control Systems Technology, 2009, 17(4): 803-815.
［19］Tian W N. Research on lateral control algorithm of self-driving vehicle ［D］. Changchun: Jilin University, 2018. (in Chinese)
[20］Liu C L, Lin H Z, Li Y M, et al. Analysis on status and development trend of intelligent control technology for agricultural equipment ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(1): 1-18. (in Chinese)
［21］Bai X P, Wang Z, Hu J T, et al. Harvester group corporative navigation method based on leader-follower structure ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2017, 48(7): 14-21. (in Chinese)
［22］Zhang S Y. Simulation research on path planning and tracking control for intelligent vehicles ［D］. Changchun: Jilin University, 2018. (in Chinese)
［23］Lenain R, Thuilot B, Cariou C, et al. Model predictive control for vehicle guidance in presence of sliding: application to farm vehicles path tracking ［C］// IEEE International Conference on Robotics & Automation. IEEE, 2005.
［24］Szepe T, Assal S F M. Pure pursuit trajectory tracking approach: Comparison and experimental validation ［J］. International Journal of Robotics & Automation, 2012, 27(4): 355-363.
［25］Wang W J, Hsu T M, Wu T S. The improved pure pursuit algorithm for autonomous driving advanced system ［C］//2017 IEEE 10th International Workshop on Computational Intelligence and Applications (IWCIA). IEEE, 2017: 33-38.
［26］Zhang Z G, Luo X W, Zhao Z X, et al. Trajectory tracking control method based on Kalman filter and pure pursuit model for agricultural vehicle ［J］. Transactions of the Chinese Society for Agricultural Machinery, 2009, 40(Z1): 6-12. (in Chinese)

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智能收获机器人侧滑及滞后性控制策略研究

Research on the control strategy of sideslip and hysteresis of intelligent harvesting robot

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