Speed control strategy for tractor assisted driving based on chassis dynamometer test
Keywords:
tractor, chassis dynamometer, assisted driving, speed control, fuzzy PIDAbstract
Realizing automation of the chassis dynamometer and the unmanned test workshop is an inevitable trend in the development of new tractor products. The accuracy of the speed control of the test tractor directly affects the accuracy of the test loading force. In order to meet the purpose of precise control of the test tractor speed on the chassis dynamometer, a fuzzy PID control strategy was developed according to the working principle of assisted driving. On the basis of traditional PID control, the parameters of fuzzy inference module were added for real-time adjustment to achieve faster response to tractor speed changes and more precise control of tractor speed. The Matlab-Cruise co-simulation platform was established for simulation, and the experiment was verified by the tractor chassis dynamometer using the NEDC working condition and tractor ploughing working condition. The results show that both PID control and fuzzy PID control can achieve tractor speed following accuracy of ±0.5 km/h. Fuzzy PID control has higher tractor speed following accuracy, faster response when speed changes, less tractor speed fluctuation, and overall control effect is better than PID control. The research results can provide a reference for the realization of the chassis dynamometer unmanned test workshop. Keywords: tractor, chassis dynamometer, assisted driving, speed control, fuzzy PID DOI: 10.25165/j.ijabe.20211406.6380 Citation: Zhang X R, Zhou Z L. Speed control strategy for tractor assisted driving based on chassis dynamometer test. Int J Agric & Biol Eng, 2021; 14(6): 169–175.References
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[2] Xie B, Wu Z B, Mao E R. Development status and prospect of key technologies of agricultural tractors. Transactions of the CSAM, 2018; 49(8): 1–17. (in Chinese)
[3] Gonzalez D O, Martin-Gorriz B, Berrocal I I, Morales A M, Salcedo G A, Hernandez B M. Development and assessment of a tractor driving simulator with immersive virtual reality for training to avoid occupational hazards. Computers and Electronics in Agriculture, 2017; 143: 111–118.
[4] Okunev A P, Borovkov A I, Karev A S, Lebedev D O, Kubyshkin V I, Nikulina S P, et al. Digital modeling and testing of tractor characteristics. Russian Engineering Research, 2019; 39(6): 453–458.
[5] Sun Y T, Wang Y L, Zhu R F, Geng R G, Zhang J Z, Fan D H, et al. Development of test bed of hybrid electric vehicle based on chassis dynamometer. IOP Conference Series: Materials Science and Engineering, 2018; 452(4): 042112. doi: 10.1088/1757-899X/452/4/ 042112
[6] Yan X H, Zhou Z L, Jia F. Compilation and verification of dynamic torque load spectrum of tractor power take-off. Transactions of the CSAE, 2019; 35(19): 74–81. (in Chinese)
[7] Liu W. Simulation and experimental study on modeling load force of chassis dynamometer. Changchun: Jilin University, 2018. (in Chinese)
[8] Wang J, Chen G, Wang J W. Driving force simulation and optimization of shift manipulator for vehicle robot driver. Computer Simulation, 2017; 34(5): 347–352. (in Chinese)
[9] Simone C, Angela M. Performance testing of a locomotive engine after treatment pre-prototype in a passenger cars chassis dynamometer laboratory. Transportation Research Procedia, 2016; 14: 605–614.
[10] Yoshihiro N, Yoshizumi K. Determination of nitrous acid emission factors from a gasoline vehicle using a chassis dynamometer combined with incoherent broadband cavity-enhanced absorption spectroscopy. Science of the Total Environment, 2017; 575: 287–293.
[11] Zhang B J, Zhao J Y, Jiao Q H. Vehicle emission durability test bench based on driving robot. Light Vehicle Technology, 2004; Z1: 4–7. (in Chinese)
[12] Xue J L, Zhang W G, Gong Z Y. Robot driver for indoor test of vehicles. Automotive Engineering, 2007; 29(10): 893–895. (in Chinese)
[13] Zhang K L, Peng X Y, Hong B, Li Z H, Wang J J, Huang Z Y, et al. Design of vehicle robot driver based on the real-time Ethernet EtherCAT. Small Internal Combustion Engine and Vehicle Technology, 2016; 45(5): 57–60. (in Chinese)
[14] Nikolaus E R, Christian H M, Igor Š, Stefan J, Gorazd K. Automated vehicle drive away with a manual dry clutch on chassis dynamometers: Efficient identification and decoupling control. ISA Transactions, 2019.
[15] Wu X, Zhang Y, Zou T, Zhao L, Lou P H, Yin Z Y. Coordinated path tracking of two vision-guided tractors for heavy-duty robotic vehicles. Robotics and Computer- Integrated Manufacturing, 2018; 53: 93–107.
[16] Dam H P, Pongsathorn R, Masao N. Study on driver model for hybrid truck based on driving simulator experimental results. IATSS Research, 2018; 42(1): 18–23.
[17] Butzke J, Daniilidis K, Kushleyev A, Lee D D, Likhachev M, Phillips C, et al. The University of Pennsylvania MAGIC 2010 multi‐-robot unmanned vehicle system. Journal of Field Robotics, 2012; 29(5): 745–761.
[18] Chen H, Lu W, Zhao X L, Wang L, Zhang Y N, He X H. The fuzzy-adaptive PID control based on the force feedback ofthe tractor robot driver's gear shift mechanical arm. Journal of Nanjing Agricultural University, 2016; 39(1): 166–174. (in Chinese)
[19] Lu W, Chen H, Wang L, Zhao X L, Zhang Y N. Motion analysis of tractor robot driver’s gear shift mechanical Arm. Transactions of the CSAM, 2016; 47(1): 37–44. (in Chinese)
[20] Lu W, Chen H, Wang J P, Wang L, Qiu W, Deng Y M. Research on human-computer cooperation method based on tractor driving robot. Journal of Nanjing University of Information Technology (Natural Science Edition), 2019; 11(2): 165–172. (in Chinese)
[21] Li G Y, Yang L J. Neural fuzzy predictive control and its MATLAB implementation. Electronic Industry Press, 2016. (in Chinese)
[22] Yan X H, Xu L Y, Wang Y. The loading control strategy of the mobile dynamometer vehicle based on neural network PID, Mathematical Problems in Engineering, 2017. doi: 10.1155/2017/5658983
[23] Zhao Y, Xie J F, Shi J W, Li H D. Energy management strategy of FCHV considering fuel cell durabilitys. Modern Manufacturing Engineering, 2020; 4: 70–76, 158. (in Chinese)
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Published
2021-12-16
How to Cite
Zhang, X., & Zhou, Z. (2021). Speed control strategy for tractor assisted driving based on chassis dynamometer test. International Journal of Agricultural and Biological Engineering, 14(6), 169–175. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/6380
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Information Technology, Sensors and Control Systems
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