Tractor-Implement Tillage Depth Control Using Adaptive Neuro-Fuzzy Inference System (ANFIS)

Aristide Timene; Ndjiya  Ngasop; Haman  Djalo

doi:10.46604/peti.2021.7522

Authors

Aristide Timene Department of Physics, Faculty of Sciences, University of Ngaoundere, Cameroon
Ndjiya Ngasop Department of Electrical Engineering, Energy and Automation, National School of Agro-Industrial Sciences, University of Ngaoundere, Cameroon
Haman Djalo Department of Physics, Faculty of Sciences, University of Ngaoundere, Cameroon

DOI:

https://doi.org/10.46604/peti.2021.7522

Keywords:

tractor-implement, plowing depth, neuro-fuzzy controller

Abstract

This study presents a design of an adaptive neuro-fuzzy controller for tractors’ tillage operations. Since the classical controllers allows plowing depth errors due to the variations of lands structure, the use of the combined neural networks and fuzzy logic methods decreases these errors. The proposed controller is based on Adaptive Neuro-Fuzzy Inference System (ANFIS), which permits the generation of fuzzy rules to cancel the nonlinearity and disturbances on the implement. The design and simulations of the system, which consist of a hitch-implement mechanism, an electro-hydraulic actuator, and a neuro-fuzzy controller, are conducted in SolidWorks and MATLAB software. The performance of the proposed controller is analyzed and is contrasted with a Proportional Integral Derivative (PID) controller. The obtained results show that the neuro-fuzzy controller adapts perfectly to the dynamics of the system with rejection of disturbances.

References

B. Bhondave, T. Ganesan, N. Varma, R. Renu, and N. Sabarinath, “Design and Development of Electro Hydraulics Hitch Control for Agricultural Tractor,” SAE International Journal of Commercial Vehicles, vol. 10, no. 1, pp. 405-410, 2017.

J. Han, C. Xia, G. Shang, and X. Gao, “In-Field Experiment of Electro-Hydraulic Tillage Depth Draft-Position Mixed Control on Tractor,” IOP Conference Series: Materials Science and Engineering, vol. 274, no. 1, 012028, 2017.

S. M. Shafaei, M. Loghavi, and S. Kamgar, “A Practical Effort to Equip Tractor-Implement with Fuzzy Depth and Draft Control System,” Engineering in Agriculture, Environment, and Food, vol. 12, no. 2, pp. 191-203, July 2019.

C. Liu, J. Zhao, J. Gu, Y. Du, Z. Li, Z. Zhu, et al., “Pressure Control Algorithm Based on Adaptive Fuzzy PID with Compensation Correction for the Tractor Electronic Hydraulic Hitch,” Applied Sciences, vol. 10, no. 9, 3179, May 2020.

B. Haznedar and A. Kalinli, “Training ANFIS Structure Using Simulated Annealing Algorithm for Dynamic Systems Identification,” Neurocomputing, vol. 302, pp. 66-74, August 2018.

S. Subha and S. Nagalakshmi, “Design of ANFIS Controller for Intelligent Energy Management in Smart Grid Applications,” Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 6, pp. 1-11, June 2020.

J. S. Jang, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Transactions on Systems, Man, and Cybernetics, vol. 23, no. 3, pp. 665-685, May-June 1993.

V. Bakırcıoğlu, M. A. Şen, and M. Kalyoncu, “Adaptive Neural-Network Based Fuzzy Logic (ANFIS) Based Trajectory Controller Design for One Leg of a Quadruped Robot,” 5th International Conference on Mechanics and Control Engineering, December 2016, pp. 82-85.

H. Oubehar, A. Selmani, A. Ed-Dahhak, A. Lachhab, M. E. H. Archidi, and B. Bouchikhi, “ANFIS-Based Climate Controller for Computerized Greenhouse System,” Advances in Science Technology and Engineering Systems Journal, vol. 5, no. 1, pp. 8-12, January 2020.

R. Shanthi, S. Kalyani, and P. M. Devie, “Design and Performance Analysis of Adaptive Neuro-Fuzzy Controller for Speed Control of Permanent Magnet Synchronous Motor Drive,” Soft Computing, vol. 25, no. 2, pp. 1519-1533, January 2021.

H. Bentaher, E. Hamza, G. Kantchev, A. Maalej, and W. Arnold, “Three Point Hitch Mechanism Instrumentation for Tillage Power Optimization,” Biosystems Engineering, vol. 100, no. 1, pp. 24-30, May 2008.

Agricultural Wheeled Tractors-Rear Mounted Three-Point Linkage—Categories 1N, 1, 2N, 2, 3N, 3, 4N, and 4, GOST ISO 730-2019, 2020.

Agricultural Machinery Management Data, ASAE D497.7, 2011.

Bosch Rexroth AG, “Hitch Control Valves EHR5-OC, EHR5-LS, EHR23-EM2 RE,” https://www.naptechniek.nl/images/catalogus/pdf/EHR23_re66125_2013-07.pdf, August 19, 2020

L. Eriksson, T. Oksanen, and K. Mikkola, “PID Controller Tuning Rules for Integrating Processes with Varying Time-Delays,” Journal of the Franklin Institute, vol. 346, no. 5, pp. 470-487, June 2009.