Research progress on the slip ratio control methods for wheeled/tracked harvesters

Authors

  • Yiyang Du 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China; 2. College of Engineering, Nanjing Agricultural University, Nanjing 211800, China;
  • Wenxiang Xu 2. College of Engineering, Nanjing Agricultural University, Nanjing 211800, China;
  • Haolu Liu 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China;
  • Xinyuan Yue 2. College of Engineering, Nanjing Agricultural University, Nanjing 211800, China;
  • Maohua Xiao 2. College of Engineering, Nanjing Agricultural University, Nanjing 211800, China;
  • Cheng Shen 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China; 3. Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

Abstract

As a key piece of agricultural mechanization equipment, harvesters play a crucial role in improving agricultural productivity and operational quality. With the increasing complexity of operating environments, especially under hilly and mountainous terrain and on wet, slippery soils, harvester slip has become an increasingly prominent problem. Excessive slip seriously impairs traction efficiency, energy consumption, operational stability, and working quality, making slip-ratio control a core technology for addressing this challenge. This paper reviews slip control methods for harvesters under complex terrain conditions, including slip-ratio observation and estimation, slip prediction, and control strategies. Different categories of slip control methods and their current applications are discussed in detail. Existing limitations are analyzed, and future research directions are proposed to provide new technical support for improving the efficiency and stability of harvesters in complex operating environments.      

Keywords: slip ratio, slip control, harvester, path tracking

DOI: 10.25165/j.ijabe.20261903.10583

Citation: Du Y Y, Xu W X, Liu H L, Yue X Y, Xiao M H, Shen C. Research progress on the slip ratio control methods forwheeled/tracked harvesters. Int J Agric & Biol Eng, 2026; 19(3): 20–30.

Author Biography

Cheng Shen, 1. Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China; 3. Key Laboratory of Modern Agricultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China

SHEN Cheng, male, was born in October 1989 in Hangzhou, China; holds a D.Eng. degree in Advanced Manufacture of Southeast University, China, and a M.S. degree in agricultural mechanization engineering of Chinese Academy of Agricultural Sciences; is an associate professor in Nanjing Institute of Agricultural Mechanization, Ministry of Agriculture and Rural Affairs of the P.R.C. (NIAM, MARA) now, and mainly engaged in research on mechanical properties of crop stalks, harvester for stalk crops, intelligent agricultural machinery, and image recognition in agriculture; is a member of the Youth Working Committee of the NIAM (2018-2022), a member of the Youth Working Committee of the Chinese Society for Agricultural Machinery (2019-2023), and the reviewer of some SCI or EI source journals such as IJABE, IAEJ, Journal of Jilin University (Engineering and Technology Edition), etc. E-mail: shencheng@caas.cn

References

[1] Lei C X, Jin C Q, Li P P, Yang X Y, Zhao Z H, Lyu L L. Development status of threshing technology and equipment of grain combine harvester. Journal of Agricultural Science and Technology, 2025; 27(5): 90. (in Chinese)

[2] Liu C, Zhang H T, Zhu J W, Wang W, Zhang C L, Chen L Q. Design and test of isolated-motor-driven cleaning system for high moisture wheat thresher. Transactions of the CSAM, 2025; 56(7): 82–93. (in Chinese)

[3] Wu Q J, Wang J J, Sun D P. Design and research of wheat seed harvester. Journal of Agricultural Mechanization Research, 2021; 43(9): 024. (in Chinese)

[4] Shan H Y, Yan Y N, Zhang J, Liu X J, Han X, Liu J. Research progress on mechanization of soybean-corn belt composite planting. Journal of Chinese Agricultural Mechanization, 2024; 45(6): 42–52. (in Chinese)

[5] Chen J, Lian Y, Li Y M. Real-time grain impurity sensing for rice combine harvesters using image processing and decision-tree algorithm. Computers and Electronics in Agriculture, 2020; 175: 105591.

[6] Ding F, Luo X W, Zhang Z G, Hu L, Wu X L, Bao K Y, et al. Dual-unloading mode autonomous operation strategy and cotransporter system for rice harvester and transporter. Engineering, 2025; 48: 220–233.

[7] Wang F M, Ni X D, Zhang Q, Guo S J, Zhou J, Chen D. Estimation of combine harvester throughput using multisensor data fusion. Computers and Electronics in Agriculture, 2025; 237: 110713.

[8] National development plan of agricultural mechanization in the tenth five-year plan. Agriculture Machinery Technology Extension, 2022; 1: 4–14. (in Chinese)

[9] Sun J B, Liu Z J, Yang F Z, Sun Q, Liu Q, Luo P X. Research review of agricultural equipment and slope operation key technologies in hilly and mountains region. Transactions of the CSAM, 2023; 54(5): 1–18. (in Chinese)

[10] Wang B Y, Zhu J X, Chai X L, Liu B, Zhang G W, Yao W. Research status and development trend of key technology of agricultural machinery chassis in hilly and mountainous areas. Computers and Electronics in Agriculture, 2024; 226: 109447.

[11] Wang X W, Yuan S Q, Jia W D. Current situation and development of agricultural mechanization in hilly and mountainous areas. Journal of Drainage and Irrigation Machinery Engineering, 2022; 40(5): 535–540.

[12] John Deere. High-end control, high efficiency and multifunction-John Deere C2400 combine harvester. Farm Machinery, 2024; 5: 24. (in Chinese)

[13] Guan Q. Case New Holland high-end equipment becomes the focus of the exhibition. Agricultural Machinery Market, 2021(11): 42–3. (in Chinese)

[14] Kubota. Kubota “Guo Ⅳ” EX-M series full-feed crawler harvester. Farm Machinery, 2023; 1: 28–29. (in Chinese)

[15] Keleshou. Harvesting expert: Keleshou D370 combine harvester. Farm Machinery, 2023; 3: 39–40. (in Chinese)

[16] Zhao C J, Ma C, Li J, Wang X M. Research status and prospects of mechanization technology for rice in hilly and mountainous areas. Transactions of the CSAE, 2025; 41(1): 1–11. (in Chinese)

[17] Han H B. Zoomlion’s intelligent agricultural machinery helps early rice harvest “accelerate running”. Agricultural Machinery Market, 2025; 8: 61. (in Chinese)

[18] The 2023 Leiwo Valley corn harvester with both stalks and ears was unveiled, and the upgrade was “more than a little”. Farm Machinery, 2023; 4: 43–44. (in Chinese)

[19] Li S C. 9 peanut harvesters, which are full of vitality. Farm Machinery, 2020; 9: 28–29. (in Chinese)

[20] Chang Fa 4YZ-5A (CF905A) self-propelled corn harvester. Farm Machinery, 2020; (10): 37. (in Chinese)

[21] Mu Z Q, Zhang B, Tian K P, Huang J C, Kong F T, Wu T. Current status and technical challenges of corn mechanized harvesting in the southwestern hilly and mountainous regions. Journal of Chinese Agricultural Mechanization, 2025; 46(11): 331–335. (in Chinese)

[22] Wang F A, Feng Y Y, Wang D, Zhang Z G, Yin G D, Shen C, et al. Turning mechanism and performance testing of crawler chassis for panax notoginseng harvester. Transactions of the CSAM, 2025; 56(8): 692–703. (in Chinese)

[23] Yang K, Li J, Chen Y, Li H, Ao Y, Lei X L. Design and testing of a liftable chassis for rice harvester. Journal of Intelligent Agricultural Mechanization, 2025; 6(1): 59–70. (in Chinese)

[24] Huang Z Y. Terrain vehicle mechanics. Beijing: Machinery Industry Press. 1985; 20p. (in Chinese)

[25] Hu J B, Li X Y, Wei C. Driving principle of armored vehicle. Beijing: Beijing Institute of Technology Press. 2020; 15p. (in Chinese)

[26] Zhu J J, Wang Z P, Zhang L, Dorrell D G. Braking/steering coordination control for in-wheel motor drive electric vehicles based on nonlinear model predictive control. Mechanism and Machine Theory, 2019; 142: 103586.

[27] Chen B. Research on traction characteristics of the rigid wheel with grousers on sandy soil. Jilin University, 2007. (in Chinese)

[28] Sun G, Gao F, Li W, Sun P. Research contents and methods of vehicle ground mechanics in deep space exploration; Proceedings of the second academic conference of the deep space exploration technology Committee of China Aerospace Society, Beijing, China, 2005. (in Chinese)

[29] Tian X F. Research on interaction between tire and ground with the numerical simulation method. National University of Defense Technology, 2012. (in Chinese)

[30] Su X T. On the applicability of cone index. Transactions of the CSAM, 1985; 1: 12–23. (in Chinese)

[31] Bekker M G. Introduction to terrain-vehicle systems. Ann Arbor: University of Michigan Press, 1969.

[32] Wong J Y, Reece A R. Prediction of rigid wheel performance based on the analysis of soil-wheel stresses part I. Performance of driven rigid wheels. Journal of Terramechanics, 1967; 4(1): 81–98.

[33] Wong J Y. Theory of ground vehicles. John Wiley, 2008. DOI: 10.1002/9781119719984

[34] Lu Z J. Present situation and prospect of shear strength research of cohesive soil. China Civil Engineering Journal, 1999; 4: 3–9. (in Chinese)

[35] Du Y H. A SWI theory and numerical research on tractive performance of military vehicle under multi-operation conditions. National University of Defense Technology, 2017. DOI: 10.27052/d.cnki.gzjgu.2017.000721 (in Chinese)

[36] Janosi Z J. Analysis and presentation of soil-vehicle mechanics data. Journal of Terramechanics, 1965; 2(3): 69–79.

[37] Wong J Y. Chapter 1 - Introduction. Terramechanics and off-road vehicle engineering (Second edition). Oxford; Butterworth-Heinemann, 2010; 1–19.

[38] Tian X F, Jiang L H, Nie X H. Research progress on soil bin test in vehicle terramechanics. Auto Sci-Tech, 2013; 2: 1–5. (in Chinese)

[39] Rodríguez-Martínez D, Van Winnendael M, Yoshida K. High-speed mobility on planetary surfaces: A technical review. Journal of Field Robotics, 2019; 36(8): 1436–55.

[40] Yang X. The design of the electric all-wheel-drive farm machinery soil bin testing car. Changchun: Jilin University, 2014. (in Chinese)

[41] Xu P. Development of agricultural machinery field soil trough system. South China Agricultural University, 2020. DOI: 10.27152/d.cnki.ghanu.2020.000354 (in Chinese)

[42] Perumpral J V, Liljedahl J B, Perloff W H. A numerical method for predicting the stress distribution and soil deformation under a tractor wheel. Journal of Terramechanics, 1971; 8(1): 9–22.

[43] Schmid I C. Interaction of vehicle and terrain results from 10 years research at IKK. Journal of Terramechanics, 1995; 32(1): 3–26.

[44] Bai Y D, Sun L Y, Zhang M L, Liu X Y, Li Z L. Progress of research on terramechanics for tracked mobile robots. Journal of Machine Design, 2020; 37(10): 1–13. (in Chinese)

[45] Cundall P A. A computer model for simulating progressive large-scale movements in blocky rock systems. Procintsympon Rock Fracture, 1971; 1(ii-b): 11–18.

[46] Oida A, Schwanghart H, Ohakubo S. Effect of tire lug cross section on tire performance simulated by distinct element method. 1999.

[47] Kim J T, Hwang H, Lee H S, Park Y J. Development of DEM–ANN-based hybrid terramechanics model considering dynamic sinkage. Journal of Terramechanics, 2024; 116: 100989.

[48] Zhao C L, Zang M Y. Application of the FEM/DEM and alternately moving road method to the simulation of tire-sand interactions. Journal of Terramechanics, 2017; 72: 27–38.

[49] Liu H, Li T, Nie Y, Xie N. Study of torque distribution strategy based on slip ratio prediction. Automobile Technology, 2020; 9: 39–44. (in Chinese)

[50] Zhao X, Lu E, Tang Z, Luo C M, Xu L Z, Wang H. Trajectory prediction method for agricultural tracked robots based on slip parameter estimation. Computers and Electronics in Agriculture, 2024; 222: 109057.

[51] Sun Y J. Design of wheeled robot adaptive odometer based on terrain classification. Shijiazhuang Railway University, 2023. DOI: 10.27334/d.cnki.gstdy.2023.000845 (in Chinese)

[52] Zhong Y, Xue M Q, Yuan H L. Design of the GNSS/INS integrated navigation system for intelligent agricultural machinery. Transactions of the CSAE, 2021; 37(9): 40–46. (in Chinese)

[53] Jiang B, Yin H F. Research and implementation of tractor speed measurement method based on Beidou navigation system. Farm Machinery, 2019; 10: 83–85. (in Chinese)

[54] Zheng D J. Application of automatic speed control system based on K210 visual identification module. Equipment Technology, 2025; 4: 38–41. (in Chinese)

[55] Du X Q, Hong F W, Ma K H, Li Y C, Zhao L J. State-of-the-art and prospect on sliding identification and control of agricultural machinery. Transactions of the CSAM, 2024; 55(8): 1–20. (in Chinese)

[56] Zhou K, Lei S S, Du X Z. Modelling and dynamic analysis of slippage level for large-scale skid-steered unmanned ground vehicle. Scientific Reports, 2022; 12(1): 16014.

[57] Tang S X, Yuan S H, Hu J B, Li X Y, Zhou J J, Guo J. Modeling of steady-state performance of skid-steering for high-speed tracked vehicles. Journal of Terramechanics, 2017; 73: 25–35.

[58] Zhang J X, Zhou S Y, Zhao J, Shi T. Wheel slip rate tracking control based on nonlinear disturbance observer. J Huazhong Univ Sci Technol Nat Sci Ed, 2020; 48: 44–49. (in Chinese)

[59] Vlakhova A V, Novoderova A P. A variable-structure model for studying skidding of a four-wheeled vehicle with slipping wheels. Moscow University Mechanics Bulletin, 2024; 79(3): 75–81.

[60] Zhuang Y, Song Z S, Gao X L, Yang X G, Liu W P. A combined-slip physical tire model based on the vector distribution considering tire anisotropic stiffness. Nonlinear Dynamics, 2022; 108(4): 2961–2976.

[61] Wada M, Kang Sup Y, Hashimoto H. High accuracy road vehicle state estimation using extended Kalman filter. Proceedings of the ITSC2000 IEEE Intelligent Transportation Systems Proceedings (Cat No00TH8493), 2000; Oct 1–3. DOI: 10.1109/ITSC.2000.881069

[62] Tong L. An approach for vehicle state estimation using extended Kalman filter. Springer, Berlin Heidelberg, 2012.

[63] Anh Tuan L, Rye D C, Durrant-Whyte H F. Estimation of track-soil interactions for autonomous tracked vehicles. Proceedings of the International Conference on Robotics and Automation, 1997; April 25. DOI: 10.1109/ROBOT.1997.614331

[64] Song X, Seneviratne L D, Althoefer K. Slip parameter estimation for tele-operated ground vehicles in slippery terrain. Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, 2011; 225(6): 814–830.

[65] Alshawi A, De Pinto S, Stano P, Van Aalst S, Praet K, Boulay E, et al. An adaptive unscented Kalman filter for the estimation of the vehicle velocity components, slip angles, and slip ratios in extreme driving manoeuvres. Sensors, 2024; 24(2): 436.

[66] Zhang J X, Shi Z T, Yang X, Zhao J. Wheel slip tracking control of vehicle based on Elman neural network. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2020; 48(6): 64–69. (in Chinese)

[67] Kang Y T, Xue B, Zeng R Y. Self-adaptive path tracking control for mobile robots under slippage conditions based on an RBF neural network. Algorithms, 2021; 14(7): 196.

[68] Chrosniak J, Ning J, Behl M. Deep dynamics: Vehicle dynamics modeling with a physics-constrained neural network for autonomous racing. IEEE Robotics and Automation Letters, 2024; 9(6): 5292–5297.

[69] Xu W X, Zhu Y J, Xiao M H, Liu M N, Ye L L, Yang Y P, et al. Energy-saving and stability-enhancing control for unmanned distributed drive electric plant protection vehicle based on active torque distribution. Artificial Intelligence in Agriculture, 2025; 11: 004.

[70] Chen J W, Pan W, Cao T, Qian Z Y, Zhang L, Chen X B, et al. Auxiliary-enhanced neural networks for electric vehicle dynamics: Advancing tire force and state estimation during wheel slip. Control Engineering Practice, 2025; 165: 106550.

[71] Zhang H, Xv K, Li Y H, Zhao W Z, Wang C Y. Coordination control strategy of motion stability and tire slip for electric vehicle driven by in-wheel-motors. Journal of the Franklin Institute, 2024; 361(15): 107126.

[72] Ma H. Vision based lunar/Mars rover slip prediction research. University of Chinese Academy of Sciences (Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences), 2019. DOI: 10.27612/d.cnki.gyyys.2019.000002 (in Chinese)

[73] Xia L M, Changjiang W, Zhao K B. Prediction of sliding of ground wheel of the planter based on BP neural network. Journal of Agricultural Mechanization Research, 2012; 34(11): 167–170. (in Chinese)

[74] Shafaei S M, Loghavi M, Kamgar S. Feasibility of implementation of intelligent simulation configurations based on data mining methodologies for prediction of tractor wheel slip. Information Processing in Agriculture, 2019; 6(2): 183–199.

[75] Angelova A, Matthies L, Helmick D, Perona P. Learning and prediction of slip from visual information. Journal of Field Robotics, 2007; 24(3): 205–231.

[76] Xiong L, Yang X, Zhuo G, Leng B, Zhang R X. Review on motion control of autonomous vehicles. Journal of Mechanical Engineering, 2020; 56(10): 127–143. (in Chinese)

[77] Zhang H Q, Wang G D, Lyu Y F, Qin C L, Liu L, Gong J L. Agricultural machinery automatic navigation control system based on improved pure tracking model. Transactions of the CSAM, 2020; 51(9): 18–25. (in Chinese)

[78] Gao X, Zhang J F, Zhao R, Yang X H, Liu J B. Research on path tracking controller of crawler-type cabbage harvester based on IPSO-FUZZY-PP. Journal of Intelligent Agricultural Mechanization, 2026; 7(1): 86–95. (in Chinese)

[79] He J, Man Z X, Hu L, Luo X, Wang P, Li M, et al. Path tracking control method and experiments for the crawler-mounted peanut combine harvester. Trans Chin Soc Agric Eng, 2023; 39: 9–17. (in Chinese)

[80] Lenain R, Thuilot B, Cariou C, Martinet P. Mobile robot control in presence of sliding: Application to agricultural vehicle path tracking; Proceedings of the 45th IEEE Conference on Decision and Control, 2006; Dec. 13–15. DOI: 10.1109/CDC.2006.377520

[81] Li M, Wang Y, Tang Y L, Wang P, Liu L Q, Xia Y. Course control technology of unmanned agricultural machinery considering body shake and slip. China Southern Agricultural Machinery, 2024; 55(8): 1–4, 11. (in Chinese)

[82] Yang W S, Su Y X, Chen Y P, Lian C. Integrated spatial kinematics–dynamics model predictive control for collision-free autonomous vehicle tracking. Actuators, 2024; 13(4): 153.

[83] Ye B L, Niu S F, Li L X, Wu W M. A comparison study of kinematic and dynamic models for trajectory tracking of autonomous vehicles using model predictive control. International Journal of Control, Automation and Systems, 2023; 21(9): 3006–3021.

[84] Wu Y, Wang C, Dong G X, Zeng R Y, Cao K, Cao D P. Slip-aware adaptive trajectory tracking control strategy for autonomous tracked vehicle. Journal of Mechanical Engineering, 2024; 60(24): 211–25. DOI: 10.3901/JME.2024.24.211 (in Chinese)

[85] Wang Z J. Path tracking model predictive control for 4WID high gap sprayer considering driving wheel slip. Jiangsu University, 2022. DOI: 10.27170/d.cnki.gjsuu.2022.002047 (in Chinese)

Downloads

Published

2026-07-14

How to Cite

(1)
Du, Y.; Xu, W.; Liu, H.; Yue, X.; Xiao, M.; Shen, C. Research Progress on the Slip Ratio Control Methods for Wheeled Tracked Harvesters. Int J Agric & Biol Eng 2026, 19, 20-30.

Issue

Section

Overview Articles