https://doi.org/10.4081/jae.2023.1530
Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network
Published: 5 July 2023
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
To study the influence of speed factors on the stability of a tractor automatic navigation system, combined with the neural network control theory, the authors proposed a dual-objective joint sliding mode control method based on lateral position deviation and heading angle deviation, using a back propagation neural network to establish a two-wheel tractor-path dynamics model and a straight-line path tracking deviation model. The overall system simulation was carried out using Matlab/Simulink, and the reliability of the control method was verified. The experimental results showed that when the tractor was tracked with the automatic control of a linear path under the condition of variable speed, the maximum deviation of the lateral position deviation was 12.7 cm, and the average absolute deviation was kept within 4.88 cm; the maximum deviation of the heading angle deviation was 5°, and the average absolute deviation was kept within 2°; the maximum value of the actual rotation angle was 3.13°, and the standard deviation of the fluctuation was within 0.84°. Under the conditions of constant speed and variable speed, using the joint sliding mode control method designed by the authors, the dual-objective joint control of lateral position deviation and heading angle deviation could be realized, the controlled overshoot was small, the controlled deviation was small after reaching a stable state, and the adaptability to speed factors was strong, which basically could meet the accuracy requirements of farmland operations.
Antonov, D., Kolganov, L., Savkin, A. , Chekhov, E., Ryabinkin, M. 2020. Navigation and motion control systems of the autonomous underwater vehicle. EUREKA Physics and Engineering, 4(4), 38-50.
DOI: https://doi.org/10.21303/2461-4262.2020.001361
Bitar, N. A., Gavrilov, A., Khalaf, W.. 2020. Artificial intelligence based methods for accuracy improvement of integrated navigation systems during gnss signal outages: an analytical overview. Gyroscopy and Navigation, 11(1), 41-58.
DOI: https://doi.org/10.1134/S2075108720010022
Chen, C. H., Lin, C. J., Jeng, S. Y. , Lin, H. Y., Yu, C. Y. 2021. Using ultrasonic sensors and a knowledge-based neural fuzzy controller for mobile robot navigation control. Electronics, 10(4), 466.
DOI: https://doi.org/10.3390/electronics10040466
Gan, Y., Zhong, Y. 2021. Research on automatic control system of mr damper based on neural network algorithm. Journal of Physics: Conference Series, 1848(1), 012152 (6pp).
DOI: https://doi.org/10.1088/1742-6596/1848/1/012152
Ghthwan, A. S., Al-Hayder, A. A., Hassooni, A. S. 2020. Hybrid iwopso optimization based marine engine rotational speed control automatic system. International Journal of Electrical and Computer Engineering, 10(1), 840.
DOI: https://doi.org/10.11591/ijece.v10i1.pp840-848
Gu, Z., Li, Q. 2020. Half-voting random forest algorithm and its application in indoor pedestrian navigation. Automatic Control and Computer Sciences, 54(2), 100-109.
DOI: https://doi.org/10.3103/S0146411620020054
Han Keli, Zhu Zhongxiang, Mao Enrong. 2016. Joint control method of speed and heading of navigation tractor based on optimal control. Transactions of the Chinese Society for Agricultural Machinery (Transactions of the CSAM), 44(2): 1－7.
He, Y., Sun, R., Wang,Y., D Nie, Wang, W. 2021. Construction and application of virtual experiment teaching system for integrated mapping of unmanned ship. IOP Conference Series Earth and Environmental Science, 784(1), 012007.
DOI: https://doi.org/10.1088/1755-1315/784/1/012007
Jsa, C. , Syab, C. , Fxa, C. 2020. Control failure of the roll-isolated inertial navigation system under large pitch angle. Chinese Journal of Aeronautics, 33( 10), 2707-2715.
DOI: https://doi.org/10.1016/j.cja.2019.08.026
Komaha, V.,Yelenych, A. 2020. Direction of innovative development of navigation system in the composition of agricultural equipment. ENGINEERING ENERGY TRANSPORT , AIC(2(109)), 57-63.
DOI: https://doi.org/10.37128/2520-6168-2020-2-6
Li, A., Xu, J., Ma, F., Li, X. 2020. Design of quality control system for fighting machine operation based on Beidou navigation. IOP Conference Series: Earth and Environmental Science, 440(3), 032046 (9pp).
DOI: https://doi.org/10.1088/1755-1315/440/3/032046
Liu H, Li, X., Xu, W., Liu, Y., Liu, K., Zhang, Y. 2020. Control system of imu calibration equipment based on multi control method. Journal of Physics: Conference Series, 1570(1), 012088 (5pp).
DOI: https://doi.org/10.1088/1742-6596/1570/1/012088
Liu J. 2017.Research on Automatic Navigation Control System of Tractor Based on Speed Adaptation.Beijing: China Agricultural University, China.
Liu P, Chen Jun, Zhang Mingying. 2011. Automatic control system of orchard tractor based on laser navigation. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 27(3): 196－199.
Nosov, P., Zinchenko, S., Ben, A., Prokopchuk, Y., Kruglyj, D. 2021. Navigation safety control system development through navigator action prediction by data mining means. Eastern-European Journal of Enterprise Technologies, 2(9 (110)), 55-68.
DOI: https://doi.org/10.15587/1729-4061.2021.229237
Pei, P. , Petrenko, Y. N. 2020. Mobile robot automatic navigation control algorithm based on fuzzy neural network in industrial internet of things environment. Informatics and Education, 1(1), 59-67.
DOI: https://doi.org/10.32517/0234-0453-2020-35-1-59-67
Rubanov, V., Bushuev, D., Karikov, E., Bazhanov, A., Alekseevsky, S. 2020. Development a low-cost navigation technology based on metal line sensors and passive rfid tags for industrial automated guided vehicle. Journal of Engineering and Applied Sciences, 15(20), 2291-2297.
Sikarev, I. A., Chistyakov, G. B., Garanin, A. V., Moskvin, D. A. 2020. Algorithms for enhancing information security in the processing of navigation data of unmanned vessels of the technical fleet of the inland waterways of the russian federation. Automatic Control and Computer Sciences, 54(8), 964-967.
DOI: https://doi.org/10.3103/S0146411620080325
Sikarev, I. A., Sakharov, V. V., Garanin, A.V. 2020. On improving the reliability and information security of information transmission systems in communication channels of an unmanned vessel. Automatic Control and Computer Sciences, 54(8), 896-899.
DOI: https://doi.org/10.3103/S0146411620080313
Someswari, T., Tiwari, A. K., Nagraj, R. 2020. A dynamic cruise control system (dccs) for effective navigation system. International Journal of Electrical and Computer Engineering, 10(5), 4645.
DOI: https://doi.org/10.11591/ijece.v10i5.pp4645-4654
Tan Chenjiao, Li Yilin, Wang Dongfei, Mao Wenju, Yang Fuzeng. 2020. Review on Automatic Navigation Technologies of Agricultural Machinery. Journal of Agricultural Mechanization Research , 5: 7－14．
Tanino, H., Nishida, Y., Mitsutake, R., Ito, H. 2020. Portable accelerometer-based navigation system for cup placement of total hip arthroplasty: a prospective, randomized, controlled study. The Journal of Arthroplasty, 35(1), 172-177.
DOI: https://doi.org/10.1016/j.arth.2019.08.044
Xu, N., Song, Y., Zhang, Q. 2021. Research on automatic control of agricultural robot trajectory optimization based on mathematical model. Journal of Physics: Conference Series, 1992(2), 022163 (6pp).
DOI: https://doi.org/10.1088/1742-6596/1992/2/022163
Yan, Z. , Zhang, X., Zhu, H., Li, Z. 2020. Course-keeping control for ships with nonlinear feedback and zero-order holder component. Ocean Engineering, 209(3), 107461.
DOI: https://doi.org/10.1016/j.oceaneng.2020.107461
Zayats Р.V ., Malevich, I.Y. 2021. Increasing the noise immunity of radio receiving paths with automatic sensitivity control. Doklady BGUIR, 19(2), 74-82.
DOI: https://doi.org/10.35596/1729-7648-2021-19-2-74-82
Zhang, S., Guo, C., Gao, Z., Sugirbay, A., & Chen, Y. 2020. Research on 2d laser automatic navigation control for standardized orchard. Applied Sciences, 10(8), 2763.
DOI: https://doi.org/10.3390/app10082763
Zhibo Lian, Junyong Zhai. 2021.Sliding Mode Control for Robot Manipulators with Actuator Faults. Proceedings of the 40th Chinese Control Conference，3797-3802.
DOI: https://doi.org/10.23919/CCC52363.2021.9550388
Zhiqiang Li , Liqing Chen , Quan Zheng , Xianyao Dou , Lu Yang . 2019. Control of a path following caterpillar robot based on a sliding mode variable structure algorithm. Biosystems Engineering, 186, 293-306.
DOI: https://doi.org/10.1016/j.biosystemseng.2019.07.004
Zhiqiang Li, Weiwei Wang, Chunling Zhang, Quan Zheng, Lichao Liu. 2023. Fault-tolerant control based on fractional sliding mode: Crawler plant protection robot. Computers and Electrical Engineering, 105, 108527.
DOI: https://doi.org/10.1016/j.compeleceng.2022.108527
Zinchenko, S., Mateichuk, V., Nosov, P., Popovych, I., Grosheva, O. 2020. Use of simulator equipment for the development and testing of vessel control systems. Electrical Control and Communication Engineering, 16(2), 58-64.
DOI: https://doi.org/10.2478/ecce-2020-0009
Zhu Y., Shen Y. 2017. Automatic Control of Trajectory Optimization for Agricultural Ｒobot—Based on BP Neural Network and Computational Torque. Journal of Agricultural Mechanization Research , 39( 6) : 33－37．
Zhuo, C., He, J., Hao, R., Ren, L. 2021. Temperature experiment and compensation algorithm design for fiber gyros in rapid startup inertial navigation system. Journal of Physics: Conference Series, 1887(1), 012004 (8pp).
DOI: https://doi.org/10.1088/1742-6596/1887/1/012004
How to Cite
Sun, Y. and Yi, K. (2023) “Agricultural machinery photoelectric automatic navigation control system based on back propagation neural network”, Journal of Agricultural Engineering, 54(4). doi: 10.4081/jae.2023.1530.
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