Fuzzy Control Design for Steering and Speed in Autonomous Agricultural Vehicles: A Multibody Simulation Study

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 10

Year of Publication : 2025

Authors : Gabriel Moreano, Sergio Villacrés, Cristian Redrobán, Mayra Viscaino

10.14445/23488379/IJEEE-V12I10P117

How to Cite?

Gabriel Moreano, Sergio Villacrés, Cristian Redrobán, Mayra Viscaino, "Fuzzy Control Design for Steering and Speed in Autonomous Agricultural Vehicles: A Multibody Simulation Study," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 10, pp. 232-245, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I10P117

Abstract:

For the agricultural industry, plants and crops are not uniformly distributed in terms of their species populations or physical structure. The autonomous vehicles for agricultural farming should have adaptive control which is able to handle soil variation and environmental variations. The scope of this work was to design a non-linear controller for an agricultural vehicle based on fuzzy logic and its simulation through a multi-body model. The major purpose was to establish the vehicle multi-body model, verify that the fuzzy control is efficient for this non-analyzed formula, as well as estimate its coherence with the navigation mark identification algorithm. The control was exerted on two parameters, the longitudinal velocity of the car and the deviation angle. The error to be corrected was monitored on real test images, saved in a database. The inputs of the controller are position and orientation errors representing the current lateral error to be corrected and predictive lateral error correction, respectively. The controller output was used to sharpen the outputs in order to reduce the computational cost of the algorithm. With this setup, the vehicle's reaction was quick as it approached the target line and longer as the vehicle moved further away from it to keep a proportionate level of precision and smoothness. Its performance was tested on a simulation of the multi-body model of the vehicle, on some trajectories (straight, circular, sinusoidal). In all scenarios, the controller was able to steer their vehicle with low lateral deviation and a natural feeling of steering. The addition of white noise allowed the controller to navigate successfully without apparent disturbance. The next step will be real experiments, and a vision system implemented in an economic platform for the detection algorithm. The findings show that this fuzzy system can be implemented as real time in some simple and cheap equipment. This could make it a realistic option for self-driving vehicles that can do different agricultural jobs.

Keywords:

Fuzzy Control, Autonomous Vehicle, Tracking Control, Computer Vision, Precision Agriculture.

References:

[1] George Mgendi, “Unlocking the Potential of Precision Agriculture: Benefits, Challenges, and Future Directions,” Discover Agriculture, vol. 2, no. 1, pp. 1-24, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Sofia Bahmutsky et al., “A Review of Life Cycle Impacts and Costs of Precision Agriculture for Field Crops,” Sustainable Production and Consumption, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Jiahui Xu et al., “The Evolution of Precision Agriculture and Food Safety,” Frontiers in Sustainable Food Systems, vol. 8, pp. 1-26, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[4] James Lowenberg-DeBoer, and Bruce Erickson, “Setting the Record Straight on Precision Agriculture Adoption,” Agronomy Journal, vol. 111, no. 4, pp. 1552-1569, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Sofia Bahmutsky et al., “A Review of Life Cycle Impacts and Costs of Precision Agriculture for Field Crops,” Sustainable Production and Consumption, vol. 52, pp. 347-362, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Mary Sanyaolu, and Arkadiusz Sadowski, “The Role of Precision Agriculture Technologies in Enhancing Sustainable Agriculture,” Sustainability, vol. 16, no. 15, pp. 1-16, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Johannes Munz, “What if Precision Agriculture is Not Profitable?: A Comprehensive Analysis of the Right Timing for Exiting, Taking into Account Different Entry Options,” Precision Agriculture, vol. 25, pp. 1284-1323, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[8] Dorijan Radočaj, Ivan Plaščak, and Mladen Jurišić, “Global Navigation Satellite Systems as State‑of‑the‑Art Solutions in Precision Agriculture: A Review of Studies Indexed in the Web of Science,” Agriculture, vol. 13, no. 7, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Jing Guo et al., “Multi‑GNSS Precise Point Positioning for Precision Agriculture,” Precision Agriculture, vol. 19, no. 5, pp. 895-911, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Vijendra Kumar, “A Comprehensive Review on Smart and Sustainable Agriculture using IoT Technologies,” Smart Agricultural Technology, vol. 8, pp. 1-22, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Sheikh Mansoor et al., “Integration of Smart Sensors and IoT in Precision Agriculture: Trends, Challenges and Future Perspectives,” Frontiers in Plant Science, vol. 16, pp. 1-21, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[12] Tymoteusz Miller et al., “The IoT and AI in Agriculture: The Time Is Now-A Systematic Review,” Sensors, vol. 25, no. 12, pp. 1-32, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[13] N.S. Abu et al., “Internet of Things Applications in Precision Agriculture: A Review,” Journal of Robotics and Control, vol. 3, no. 3, pp. 338-347, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Gourab Saha et al., “Smart IoT‑Driven Precision Agriculture: Land Mapping, Crop Prediction, and Irrigation System,” Plos One, vol. 20, no. 3, pp. 1-33, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Gopal Tiwari et al., “Terrain‑Integrated Soil Mapping Units (SMUs) for Precision Nutrient Management: A Case Study from Semi‑Arid Tropics of India,” NDT-Journal of Non-Destructive Testing, vol. 3, no. 3, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Robina Kousar et al., “Farm‑Scale Soil Spatial Variability at a Mountain Research Farm Provides Basis for Precision Agriculture,” Scientific Reports, vol. 15, no. 1, pp. 1-22, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Megha Sharma, Shailendra Goel, and Ani A. Elias, “Predictive Modeling of Soil Profiles for Precision Agriculture,” Scientific Reports, vol. 15, no. 1, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Hongxuan Wu et al., “Review on Key Technologies for Autonomous Navigation in Field Agricultural Machinery,” Agriculture, vol. 15, no. 12, pp. 1-28, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Md. Sabbir Hossain et al., “Automatic Navigation and Self‑Driving Technology in Agricultural Machinery: A State‑of‑the‑Art Systematic Review,” IEEE Access, vol. 13, pp. 94370-94401, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Canicius Mwitta, and Glen C. Rains, “The Integration of GPS and Visual Navigation for Autonomous Navigation of an Ackerman Steering Mobile Robot in Cotton Fields,” Frontiers in Robotics and AI, vol. 11, pp. 1-19, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Shivam K. Panda, Yongkyu Lee, and M. Khalid Jawed, “Agronav: Autonomous Navigation Framework for Agricultural Robots and Vehicles using Semantic Segmentation and Semantic Line Detection,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 6272-6281, 2023.
[Google Scholar] [Publisher Link]
[22] Zhixin Yao, Chunjiang Zhao, and Taihong Zhang, “Agricultural Machinery Automatic Navigation Technology-A Comprehensive Review,” Applied Sciences, vol. 12, no. 13, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[23] Andrea Grazioso et al., “Multibody Modeling of a New Wheel/Track Reconfigurable Locomotion System for Farming Vehicles,” Machines, vol. 10, no. 12, pp. 1-29, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Filippo Califano et al., “Multibody Model for the Design of a Rover for Agricultural Applications: A Preliminary Study,” Machines, vol. 10, no. 4, pp. 1-15, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[25] Alessandro Febbraro et al., “Object-Oriented Modelling of a Tracked Vehicle for Agricultural Applications,” Computers and Electronics in Agriculture, vol. 230, pp. 1-17, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Quanyu Wang et al., “Modelling and Control Methods in Path Tracking Control for Autonomous Agricultural Vehicles: A Review of State of the Art and Challenges,” Applied Sciences, vol. 13, no. 12, pp. 1-22, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[27] Rajan Prasad, Adesh Kumar Srivastava, and Rajinder Tiwari, “Fuzzy Logic‑Based Sprinkler Controller for a Precision Irrigation System: A Case Study of Semi‑Arid Regions in India,” Engineering Proceedings, vol. 82, no. 1, pp. 1-12, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Abdennabi Morchid et al., “Fuzzy Logic-Based IoT System for Optimizing Irrigation with Cloud Computing: Enhancing Water Sustainability in Smart Agriculture,” Smart Agricultural Technology, vol. 11, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hayat Ait dahmad et al., “IT‑2 Fuzzy Control and Behavioral Approach Navigation System for Holonomic 4WD/4WS Agricultural Robot,” International Journal of Computers Communications & Control, vol. 19, no. 3, pp. 1-18, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Aylin Erdoğdu et al., “Combining Fuzzy Logic and Genetic Algorithms to Optimize Cost, Time and Quality in Modern Agriculture,” Sustainability, vol. 17, no. 7, pp. 1-44, 2025.
[CrossRef] [Google Scholar] [Publisher Link]
[31] MathWorks, Import SolidWorks Assemblies into Simscape Multibody, 2023. [Online]. Available: https://www.mathworks.com/videos/import-solidworks-assemblies-into-simscape-multibody-1701670328083.html
[32] MathWorks Documentation, SolidWorks-MATLAB & Simulink, 2025. [Online]. Available: https://www.mathworks.com/help/smlink/solidworks.html
[33] Jean Marie Lauhic Ndong Mezui et al., “SolidWorks/Simscape Multibody Co‑Simulation of the Dynamic Model of a Mobile Manipulator System,” International Journal of Research in Engineering, Science and Management, vol. 6, no. 1, pp. 1-9, 2023.
[Google Scholar] [Publisher Link]