Hostname: page-component-669899f699-chc8l Total loading time: 0 Render date: 2025-04-26T06:26:09.021Z Has data issue: false hasContentIssue false
  • English
  • Français

Trajectory tracking control of a mobile robot using fuzzy logic controller with optimal parameters

Published online by Cambridge University Press:  19 September 2024

Tesfaye Deme Tolossa ,
Manavaalan Gunasekaran ,
Kaushik Halder ,
Hitendra Kumar Verma ,
Shyam Sundar Parswal ,
Nishant Jorwal ,
Felix Orlando Maria Joseph Open the ORCID record for Felix Orlando Maria Joseph [Opens in a new window]  and
Yogesh Vijay Hote
Tesfaye Deme Tolossa
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Manavaalan Gunasekaran
Affiliation:
Department of Electrical and Electronics Engineering, CIT, Combature, Tamil Nadu, India
Kaushik Halder
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Hitendra Kumar Verma
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Shyam Sundar Parswal
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Nishant Jorwal
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Felix Orlando Maria Joseph*
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
Yogesh Vijay Hote
Affiliation:
Department of Electrical Engineering, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India
*
Corresponding author: Felix Orlando Maria Joseph; Email: m.orlando@ee.iitr.ac.in
Get access Rights & Permissions [Opens in a new window]

Abstract

This work investigates the use of a fuzzy logic controller (FLC) for two-wheeled differential drive mobile robot trajectory tracking control. Due to the inherent complexity associated with tuning the membership functions of an FLC, this work employs a particle swarm optimization algorithm to optimize the parameters of these functions. In order to automate and reduce the number of rule bases, the genetic algorithm is also employed for this study. The effectiveness of the proposed approach is validated through MATLAB simulations involving diverse path tracking scenarios. The performance of the FLC is compared against established controllers, including minimum norm solution, closed-loop inverse kinematics, and Jacobian transpose-based controllers. The results demonstrate that the FLC offers accurate trajectory tracking with reduced root mean square error and controller effort. An experimental, hardware-based investigation is also performed for further verification of the proposed system. In addition, the simulation is conducted for various paths in the presence of noise in order to assess the proposed controller’s robustness. The proposed method is resilient against noise and disturbances, according to the simulation outcomes.

Keywords

wheeled mobile robotmembership functionparticle swarm optimizationfuzzy logic controllerpath trackingexperimental evaluation
Type
Research Article
Information
Robotica , Volume 42 , Issue 8 , August 2024 , pp. 2801 - 2824
Copyright
© The Author(s), 2024. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Article purchase

Temporarily unavailable

References

Haider, M. H., Ali, H., Khan, A. A., Zheng, H., Bhutta, M. U. M., Usman, S., Zhi, P. and Wang, Z., “Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System, In: IEEE Inter. Conf. on innovations and Development of Information Technologies and robotics (IDITR), (2022) pp. 93–99.Google Scholar
Alsharkawi, A., Fetyani, M. A., Ijaabo, E. M. and Khasawneh, H., “Adaptive Neuro-Fuzzy Inference System for a Three-Wheeled Omnidirectional Mobile Robot. In: IEEE International Conference on Applied Engineering (ICAE), (2020) pp. 1–6.Google Scholar
Alouache, X. and Wu, Q., “Genetic Algorithms for Trajectory Tracking of Mobile Robot Based on PID Controller,” In: 2018 IEEE 14th Inter. Conference on Intelligent Computer Communication and Processing (ICCP), (2020) pp. 237–241.Google Scholar
Antonelli, G., Chiaverini, S. and Fusco, G., “Fuzzy-logic-based approach for mobile robot path tracking,” IEEE Trans Fuzzy Syst 15(2), 211–221 (2007).CrossRefGoogle Scholar
Ko, M. H., Ryuh, B.-S., Kim, K. C., Suprem, A. and Mahalik, N. P., “Autonomous greenhouse mobile robot driving strategies from system integration perspective: Review and application,” IEEE/ASME Trans Mech 20(4), 1705–1716 (2015).CrossRefGoogle Scholar
Zhang, J., Gong, Q. and Wang, J., “Finite-time global trajectory tracking control for uncertain wheeled mobile robots,” IEEE Access 8, 187808–187813 (2020).CrossRefGoogle Scholar
Lima, G. S., Moreira, V. R. F. and Bessa, W. M., “Accurate trajectory tracking control with adaptive neural networks for omnidirectional mobile robots subject to unmodeled dynamics,” J Braz Soc Mech Sci 45(1), 48 (2023).Google Scholar
Kashyap, A. K., Lagaza, K. P. and Pandey, A., “Dynamic Path Planning for Autonomous Mobile Robot using Minimum Fuzzy Rule Based Controller with Avoidance of Moving Obstacles,” In: IEEE International Conference on Recent Innovations in Electrical, Electronics and Communication Engineering (ICRIEECE), (2018) pp. 3330–3335.Google Scholar
Kayacan, E., “Closed-loop error learning control for uncertain nonlinear systems with experimental validation on a mobile robot,” IEEE/ASME Trans Mech 24(5), 2397–2405 (2019).CrossRefGoogle Scholar
Hwang, C.-L., “Comparison of path tracking control of a car-like mobile robot with and without motor dynamics,” IEEE/ASME Trans Mech 21(4), 1801–1811 (2016).CrossRefGoogle Scholar
Asai, M., Chen, G. and Takami, I., “Neural Network Trajectory Tracking of Tracked Mobile Robot,” In: IEEE 16th Interernational Multi-Conference on Systems, Signals and Devices (SSD), (2019) pp. 225–23.Google Scholar
Wang, T.-Y. and Chang, C.-D., “Hybrid Fuzzy PID Controller Design for a Mobile Robot,” In: IEEE International Conference on Applied System Invention (ICASI), (2018) pp.650–653.Google Scholar
Chang, T. Y. and Chang, C. D., “Genetic Algorithm Based Parameters Tuning for the Hybrid Intelligent Controller Design for the Manipulation of Mobile Robot,” In: IEEE 16th International Conference on Industrial Engineering and Applications (ICIEA), (2019) pp. 810–813.Google Scholar
Cheng, J. and Wang, B.. Flocking Control of Mobile Robots via Simulated Annealing Algorithm,” In: IEEE 39th Chinese Control Conf (CCC), (2020) pp. 3931–3935.Google Scholar
Meerza, S. I. A., Islam, M. and Uzzal, D. M. M., “Q-learning Based Particle Swarm Optimization Algorithm for Optimal Path Planning of Swarm of Mobile Robots,” In: IEEE 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), (2019) pp. 1–5.Google Scholar
Chen, Y.-H. and Chang, C.-D., “An Intelligent ANFIS Controller Design for a Mobile Robot,” In: IEEE Inter. Conference on Applied system Invention (ICASI), (2018) pp. 445–448.Google Scholar
J.C.L.Barreto, S., Conceicao, A. G. S., Dorea, C. E. T., Martinez, L. and Pier, E. R., “Design and implementation of model-predictive control with friction compensation on an omnidirectional mobile robot,” IEEE/ASME Trans Mech 19(2), 467–476 (2014).CrossRefGoogle Scholar
Tan, N., Zhu, Z. and Yu, P., “Neural-Network-Based Control of Wheeled Mobile Manipulators with Unknown Kinematic Models,” In: IEEE International Symposium on Autonomous Systems, (2021) pp. 212–216.Google Scholar
Lakhmissi, C., “Comparison Between Fuzzy, Neural and Neuro-Fuzzy Controllers for Mobile Robot Path Tracking,” In: IEEE, Conference on Control, Engineering and Information Technology, (2015). PP. 1–6.(CEIT),Google Scholar
Kayacan, E. and Chowdhary, G., “Tracking error learning control for precise mobile robot path tracking in outdoor environment,” J Intell Robot Syst 95(3-4), 975–976 (2019).CrossRefGoogle Scholar
Cai, J., Jiang, H., Chen, L., Liu, J., Cai, Y. and Wang, J., “Implementation and development of a trajectory tracking control system for intelligent vehicle,” J Intell Robot Syst 94(1), 251–264 (2019).CrossRefGoogle Scholar
Zhai, J.-Y. and Song, Z.-B., “Adaptive sliding mode trajectory tracking control for wheeled mobile robots,” Int J Control 92(10), 2255–2262 (2019).CrossRefGoogle Scholar
Deremetz, M., Lenain, R. and Thuilot, B., “Path Tracking of a Two-Wheel Steering Mobile Robot: An Accurate and Robust Multi-Model off-Road Steering Strategy,” In: IEEE International Conference on Robotics and Automation (ICRA), (2018) pp. 3037–3044.Google Scholar
Kumar, S., PremKumar, P., Dutta, A. and Behera, L., “Visual motor control of a 7DOF redundant manipulator using redundancy preserving learning network,” Robotica 28(6), 795–8102010 (2010).CrossRefGoogle Scholar
Noormohammadi-Asl, A., Saffari, M. and Teshnehlab, M., “Neural Control of Mobile Robot Motion Based on Feedback Error Learning and Mimetic Structure,” In: IEEE 26th Iranian Conference on Electrical Engineering, 2018) pp. 778–783. (ICEE2018).CrossRefGoogle Scholar
Singh, N. and Panjwani, B., “Comparison of neural network and fuzzy logic control for nonlinear model of two link rigid manipulator,” Int J Control Autom 7(4), 417–428 (2014).CrossRefGoogle Scholar
Gao, H., Wang, X. and Hu, J., “Adaptive Tracking Control of Mobile Robots Based On Neural Network and Sliding Mode Methods,” In: IEEE 38th Youth Academic Annual Conference of Chinese Association of Automation, (2023) pp. 962–967.(YAC).CrossRefGoogle Scholar
Shill, P. C., Akhand, M. A. H., Asaduzzaman, M. D. and Murase, K., “Optimization of fuzzy logic controllers with rule base size reduction using genetic algorithms,” Int J Inform Technol Decision Making 14(05), 1063–1092 (2015).CrossRefGoogle Scholar
Oltean, S. E., Dulau, M. and Puskas, R., “Position Control of Robotino Mobile Robot using Fuzzy Logic,” In: IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR), (2010) pp. 1–6.Google Scholar
Zhang, T., Zhang, W. and Gupta, M. M., “Resilient robots: Concept, review, and future directions,” Robotics 6(4), 22 (2017).CrossRefGoogle Scholar
Mohammadzadeh, A., Taghavifar, H., Zhang, Y. and Zhang, W., “A fast non singleton type-3 fuzzy predictive controller for nonholonomic robots under sensor and actuator faults and measurement errors,” In: IEEE Transactions on Systems, Man, and Cybernetics: Systems (IEEE, 2024).Google Scholar
Tiep, D. K., Lee, K., Im, D.-Y., Kwak, B. and Ryoo, Y.-J., “Design of fuzzy-PID controller for path tracking of mobile robot with differential drive,” Int J Fuzzy Log Intell Syst 18(3), 220–228 (2018).CrossRefGoogle Scholar
Maatoug, K., M., NJAH. and Jallouli, M., “IEEE 19th Inter. Conf. On Sciences and Techniques of Automatic Control and Computer Engineering (STA),” In: Electric Wheelchair Trajectory Tracking Control Based On Fuzzy Logic Controller, (2019) PP. 191–195.Google Scholar
H.M., W. U. and Zaman, M. Q., “LiDAR based trajectory-tracking of an autonomous differential drive mobile robot using fuzzy sliding mode controller,” IEEE Access 10, 33713–33722 (2022).Google Scholar
Solihina, M. I., Y.Chuana, C. and Astutib, W., “Optimization of fuzzy logic controller parameters using modern metheuristic algorithm for gantry crane system (GCS),” Mater Today Proceed 29, 168–172 (2020).CrossRefGoogle Scholar
Wu, X., Jin, P., Zou, T., Qi, Z., Xiao, H. and Lou, P., “Backstepping trajectory tracking based on fuzzy sliding mode control for differential mobile robots,” J Intell Robot Syst 96(1), 109–121 (2019).CrossRefGoogle Scholar
Khesrani, S., Hassam, A., Boutalbi, O. and Boubezoula, M., “Motion planning and control of nonholonomic mobile robot using flatness and fuzzy logic concepts,” Int J Dyn Control 9(4), 1660–1671 (2021).CrossRefGoogle Scholar
Rehman, A. U. and Cai, C., “Autonomous Mobile Robot Obstacle Avoidance using Fuzzy-PID Controller in Robot’s Varying Dynamics,” In: IEEE 39th Chinese Control Conference, (2020) pp. 2182–2186. (CCC).CrossRefGoogle Scholar
Tiep, D. K., Lee, K., Im, D., Kwak, B. and Ryoo, Y. J., “Adaptive tracking control of a nonholonomic mobile robot,” IEEE Trans Robot Autom 16(5), 609–615 (2000).Google Scholar
Lacevic, B., Velagic, J. and Osmic, N., “Design of Fuzzy Logic Based Mobile Robot Position Controller using Genetic Algorithm,” In: IEEE/ASME International Conference on Advanced Intelligent Mechatronics, (2007) pp. 1–6.Google Scholar
Singh, H., Gupta, M. M., Meitzler, T., Hou, Z.-G., Garg, K. K., Solo, A. M. G. and Zadeh, L. A., “Real-life applications of fuzzy logic,” Adv Fuzzy Syst 2013, 1–3 (2013).CrossRefGoogle Scholar
Abdelwahab, M., Parque, V., Elbab, A. M. R. F., Abouelsoud, A. A. and Sugano, S., “Trajectory tracking of wheeled mobile robots using Z-number based fuzzy logic,” IEEE Access 8, 18426–18441 (2020).CrossRefGoogle Scholar
Abubakr, O. A., Jaradat, M. A. and Abdelhafez, M. F., “Intelligent optimization of adaptive dynamic window approach for mobile robot motion control using fuzzy logic,” IEEE Access 10, 119368–119378 (2022).CrossRefGoogle Scholar
Juang, C.-F., Chou, C.-Y. and Lin, C.-T., “Navigation of a fuzzy-controlled wheeled robot through the combination of expert knowledge and data-driven multi objective evolutionary learning,” IEEE Trans Cybern 52(8), 7388–7401 (2022).CrossRefGoogle Scholar
Shojaei, K., Shahri, A. M., Tarakameh, A. and Tabibian, B., “Adaptive trajectory tracking control of a differential drive wheeled mobile robot,” Robotica 29(3), 391–402 (2011).CrossRefGoogle Scholar
Nascimento, T. P., Dórea, C. E. T. and Gonçalves, L. M. G., “Nonholonomic mobile robots trajectory tracking model predictive control: A survey,” Robotica 36(5), 676–696 (2018).CrossRefGoogle Scholar
Kumar, S. and Parhi, D. R., “Trajectory planning and control of multiple mobile robot using hybrid MKH-fuzzy logic controller,” Robotica 40(11), 3952–3975 (2022).CrossRefGoogle Scholar
Liu, H. and Dai, J., “An approach to carton-folding trajectory planning using dual robotic fingers,” Robot Auton Syst 42(1), 47–63 (2003).CrossRefGoogle Scholar
Bui, D.-N. and Phung, M. D., “Radial basis function neural networks for formation control of unmanned aerial vehicles,” Robotica 1-19(6), 1842–1860 (2024).CrossRefGoogle Scholar
Peng, S. and Shi, W., “Adaptive fuzzy output feedback control of a nonholonomic wheeled mobile robot,” IEEE Access 6, 43414–43424 (2018).CrossRefGoogle Scholar
Cheng, L., Hou, Z.-G., Tan, M. and Zhang, W. J., “Tracking control of a closed-chain five-bar robot with two degrees of freedom by integration of an approximation-based approach and mechanical design,” IEEE Trans Syst Man Cybern Part B (Cyberne) 42(5), 1470–1479 (2012).CrossRefGoogle ScholarPubMed
Korayem, M. H., Safarbali, M. and Lademakhi, N. Y., “Adaptive robust control with slipping parameters estimation based on intelligent learning for wheeled mobile robot “,” ISA Trans 147, 577–589 (2024).CrossRefGoogle ScholarPubMed