Data-driven Controller Selection for Trajectory Control of Unmanned Ground Vehicles Through an Integrated Fuzzy Decision Framework

Hayriye Tuğba Sekban; Şeyma Emeç; Abdullah Başçi

Authors

Hayriye Tuğba Sekban Department of Electronics and Automation, Bayburt University, Bayburt, Turkey Author https://orcid.org/0000-0002-3085-7297
Şeyma Emeç Department of Industrial Engineering, Erzurum Technical University, Erzurum, Turkey Author https://orcid.org/0000-0002-4881-7955
Abdullah Başçi Department of Electrical and Electronics Engineering, Ataturk University, Erzurum, Turkey Author https://orcid.org/0000-0003-4141-2880

Keywords:

Fuzzy optimization, MCDM, Decision making, Controller selection, UGV Trajectory, AHP, ARTASI

Abstract

Technological advances have made unmanned ground vehicles (UGVs) an integral part of modern life. Interest in UGVs has grown significantly, particularly in applications where human safety is a priority or where operations must be performed in environments inaccessible to humans. In recent years, research on UGVs has focused on key topics such as obstacle avoidance, path planning, mapping, localization, navigation, and trajectory tracking. Among these, trajectory tracking remains one of the most challenging problems and has attracted considerable attention from researchers. Furthermore, selecting an appropriate controller for trajectory tracking represents a critical decision-making problem. This study makes an important contribution to the literature, as comprehensive analyses employing multi-criteria decision-making (MCDM) methods for controller selection in UGV trajectory tracking remain scarce. In particular, systematic and comparative evaluations designed to support the controller selection process are still limited. In this context, this study is among the first to simultaneously apply fuzzy AHP and fuzzy ARTASI, two MCDM methods, to address the controller selection problem in UGV trajectory tracking. First, fuzzy AHP was employed to determine the weights of the evaluation criteria. Subsequently, six controllers were evaluated and ranked using the fuzzy ARTASI method based on seven evaluation criteria and linguistic assessments. Finally, the consistency and robustness of the obtained results were verified through a comparative analysis using the fuzzy MABAC, fuzzy TOPSIS, fuzzy VIKOR, and fuzzy MOORA methods. The findings indicate that a hybrid adaptive–sliding mode controller is the most suitable alternative for UGV trajectory tracking.

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References

Hassan, N., & Saleem, A. (2022). Neural network-based adaptive controller for trajectory tracking of wheeled mobile robots. IEEE Access, 10, 13582-13597. https://doi.org/10.1109/ACCESS.2022.3146970

Bai, J., Du, J., Li, T., & Chen, Y. (2022). Trajectory tracking control for wheeled mobile robots with kinematic parameter uncertainty. International Journal of Control, Automation and Systems, 20(5), 1632-1639. https://doi.org/10.1007/s12555-021-0212-z

Geng, Q., Zhao, L., Li, L., & Liu, F. (2022). A Dynamic controller design for trajectory tracking control of wheeled mobile robot under stochastic denial of service attacks. IEEE Transactions on Circuits and Systems II: Express Briefs. 69(8), 3560-3564. https://doi.org/10.1109/TCSII.2022.3168304

Wang, K., Liu, Y., Huang, C., & Cheng, P. (2020). Adaptive backstepping control with extended state observer for wheeled mobile robot. In. Proceedings of the 39th Chinese Control Conference, 1981-1986. https://doi.org/10.23919/CCC50068.2020.9188593

Tzafestas, S. G. (2018). Mobile robot control and navigation: A global overview. Journal of Intelligent & Robotic Systems, 91(1), 35-58. https://doi.org/10.1007/s10846-018-0805-9

Liu, Y., Zhong, S., Mustafa, K., Aslam, S. & Daraz, A. (2023). Design and control of a mobile robot following the desired trajectory using an adaptive sliding mode approach. https://doi.org/10.21203/rs.3.rs-751261/v4

Shi, W., Xu, L., & Chen, S. (2020, July). Adaptive dynamic surface control for simultaneous stabilization and tracking of wheeled mobile robot. In 2020 39th Chinese Control Conference (CCC) (pp. 381-386). IEEE. https://doi.org/10.23919/CCC50068.2020.9188751

Xie, C., Fan, Y. & Qiu, J. (2020). Event-based tracking control for nonholonomic mobile robots. Nonlinear Analysis: Hybrid Systems, 38, 100945. https://doi.org/10.1016/j.nahs.2020.100945

Tong, M., Lin, W., Huo, X., Jin, Z. & Miao, C. (2020). A model-free fuzzy adaptive trajectory tracking control algorithm based on dynamic surface control. International Journal of Advanced Robotic Systems, 17(1), 1-11. https://doi.org/10.1177/1729881419894417

Meng, H., Zhang, J. & Li, S. (2022). Dual‐mode robust model predictive control for the tracking control of nonholonomic mobile robot. International Journal of Robust and Nonlinear Control, 33(6), 3627-3639. https://doi.org/10.1002/rnc.6582

Lin, L., Xu, Z. & Zheng, J. (2023). Predefined time active disturbance rejection for nonholonomic mobile robots. Mathematics, 11(12), 2704. https://doi.org/10.3390/math11122704

Sekban, H.T. (2023). New adaptive control approaches for the trajectory tracking control of an unmanned ground vehi̇cle. PhD. Thesis, Ataturk University, Institute of Science, Erzurum, Türkiye.

Alouache, A. & Wu, Q. (2018). Genetic algorithms for trajectory tracking of mobile robot based on PID controller. IEEE 14th International Conference on Intelligent Computer Communication and Processing (ICCP), 237-241. https://doi.org/10.1109/ICCP.2018.8516587

Xu, L., Du, J., Song, B., & Cao, M. (2022). A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots. Systems Science & Control Engineering, 10(1), 134-141. https://doi.org/10.1080/21642583.2022.2047125

Cao, G., Zhao, X., Ye, C., Yu, S., Li, B., & Jiang, C. (2022). Fuzzy adaptive PID control method for multi-mecanum-wheeled mobile robot. Journal of Mechanical Science and Technology, 36(4), 2019-2029. https://doi.org/10.1007/s12206-022-0337-x

Rouhi Ardeshiri, R., Gheisarnejad, M., Tavan, M. R., Vafamand, N., & Khooban, M. H. (2022). A robust intelligent controller-based motion control of a wheeled mobile robot. Transactions of the Institute of Measurement and Control, 44(15), 2911-2918. https://doi.org/10.1177/01423312221088389

Moudoud, B., Aissaoui, H., & Diany, M. (2022). Extended state observer-based finite-time adaptive sliding mode control for wheeled mobile robot. Journal of Control and Decision, 9(4), 465-476. https://doi.org/10.1080/23307706.2021.2024458

Prakash, N. P. S., Perreault, T., Voth, T., & Zhong, Z. (2022). Adaptive model predictive control of wheeled mobile robots. https://doi.org/10.48550/arXiv.2201.00863

Sekban, H. T. & Başçi, A. (2022). Performing trajectory tracking control of an unmanned ground vehicle using fractional order terminal sliding mode controller. International Journal of Sustainable Aviation, 9(1), 73-88. https://doi.org/10.1504/IJSA.2023.127493

Mao, R., Tian, Y. & Dai, H. (2022). Dynamic modelling and control of differential-drive mobile robot. In International Conference on Computer Application and Information Security, 12609, 240-250. https://doi.org/10.1117/12.2672156

Dang, S.T., Dinh, X.M., Kim, T.D., Xuan, H.L. & Ha, M.H. (2023). Adaptive backstepping hierarchical sliding mode control for 3-wheeled mobile robots based on rbf neural networks. Electronics, 12(11), 2345. https://doi.org/10.3390/electronics12112345

Başçi A, & Derdiyok A. (2014). Real-Time velocity and direction angle control of an automated guided vehicle. International Journal of Robotics and Automation, 3(29), 227-233. https://dx.doi.org/10.2316/Journal.206.2014.3.206-3838

Moudoud, B., Aissaoui, H. & Diany, M. (2022a). Fuzzy adaptive sliding mode controller for electrically driven wheeled mobile robot for trajectory tracking task. Journal of Control and Decision, 9(1), 71-79. https://doi.org/10.1080/23307706.2021.1912665

Koubaa, Y., Boukattaya, M., & Dammak, T. (2015, December). Adaptive control of nonholonomic wheeled mobile robot with unknown parameters. In 2015 7th International Conference on Modelling, Identification and Control (ICMIC) (pp. 1-5). IEEE. https://doi.org/10.1109/ICMIC.2015.7409475

Modigh, F., & Pei, E. L. L. (2019). Benefits of using ıntelligent control systems in comparison to classical methods for temperature control in a greenhouse.

Nguyen, T., Nguyentien, K., Do, T. & Pham, T. (2018). Neural network-based adaptive sliding mode control method for tracking of a nonholonomic wheeled mobile robot with unknown wheel slips, model uncertainties, and unknown bounded external disturbances. Acta Polytechnica Hungarica, 15(2), 103-23. https://doi.org/10.12700/APH.15.1.2018.2.6

Van Doan, H. & Vu, N.T.T. (2023). Adaptive sliding mode control for uncertain wheel mobile robot, International Journal of Electrical and Computer Engineering, 13(4), 3939-3947. http://doi.org/10.11591/ijece.v13i4.pp3939-3947

Sekban, H.T. & Başçi, A. (2023). Designing new model-based adaptive sliding mode controllers for trajectory tracking control of an unmanned ground vehicle. IEEE Access, 11, 101387- 101397. https://doi.org/10.1109/ACCESS.2023.3313942

Zou, X., Yang, G., Hong, R., & Dai, Y. (2024). Chattering-free terminal sliding-mode tracking control for uncertain nonlinear systems with disturbance compensation. Journal of Vibration and Control, 30(5-6), 1133-1142. https://doi.org/10.1177/10775463231157213

Dong, J., Zhao, M., Cheng, M., & Wang, Y. (2023). Integral terminal sliding-mode integral backstepping adaptive control for trajectory tracking of unmanned surface vehicle. Cyber-Physical Systems, 9(1), 77-96. https://doi.org/10.2316/Journal.206.2014.3.206-3838

Xie, H., Zheng, J., Sun, Z., Wang, H. & Chai, R. (2022). Finite-time tracking control for nonholonomic wheeled mobile robot using adaptive fast nonsingular terminal sliding mode. Nonlinear Dynamics, 110(2), 1437-1453. https://doi.org/10.1007/s11071-022-07682-2

Ma, L., Wang, C., Ge, C., Liu, H. & Li, B. (2023). Fixed-time integral sliding mode tracking control of a wheeled mobile robot. Complex Engineering Systems, 3(10). https://doi.org/10.20517/ces.2023.14

Sekban, H.T. (2017). Implementation of a fractional order sliding mode controller for coupled tank liquid level control system. Master's thesis, Ataturk University Institute of Science, Erzurum Türkiye.

Xiao, X., Lv, J., Chang, Y., Chen, J. & He, H. (2022). Adaptive sliding mode control integrating with RBFNN for proton exchange membrane fuel cell power conditioning. Applied Sciences, 12(6), 3132. https://doi.org/10.3390/app12063132

Singhal, K., Kumar, V., & Rana, K. P. S. (2022). Robust trajectory tracking control of non-holonomic wheeled mobile robots using an adaptive fractional order parallel fuzzy PID controller. Journal of the Franklin Institute.

Zeng, H., & Di, Y. (2022). Trajectory tracking control of mobile robot based on interval type II fuzzy sliding mode. In 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), 1420-1425.

Dagher, K. E., Hameed, R. A., Ibrahim, I. A., & Razak, M. (2022). An adaptive neural control methodology design for dynamics mobile robot. TELKOMNIKA, 20(2), 392-404. http://doi.org/10.12928/telkomnika.v20i2.20923

Büyüközkan, G., & Çifçi, G. (2012). A Novel Hybrid MCDM Approach Based on Fuzzy DEMATEL, Fuzzy ANP And Fuzzy TOPSIS To Evaluate Green Suppliers. Expert Systems with Applications, 39 (3), 3000-3011. https://doi.org/10.1016/j.eswa.2011.08.162

Akkaya, G., Turanoğlu, B., & Öztaş, S. (2015). An integrated fuzzy AHP and fuzzy MOORA approach to the problem of industrial engineering sector choosing. Expert Systems with Applications, 42(24), 9565-9573. https://doi.org/10.1016/j.eswa.2015.07.061

Emeç, Ş., & Akkaya, G. (2018). Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem. Journal of Enterprise Information Management, 31(6), 950-962. https://doi.org/10.1108/JEIM-12-2016-0195

Emec, S., Turanoglu, B., Oztas, S., & Akkaya, G. (2019). An integrated MCDM for a medical company selection in health sector. International Journal of Scientific and Technological Research, 5(4), 77-89. https://doi.org/10.7176/JSTR/5-4-09

Kiraz, A., Gençer, N., Taş, M., & Teke, Ç. (2018). Bulanık AHP ve Bulanık TOPSIS Yöntemleri ile Sakarya İlinin Yatırım Öncelikli Sektörlerinin Belirlenmesi. Bayburt Üniversitesi Fen Bilimleri Dergisi, 1(1), 42-52. https://izlik.org/JA73CG63KP

Erkayman, B., Gundogar, E., & Yılmaz, A. (2012). An Integrated Fuzzy Approach for Strategic Alliance Partner Selection in Third‐Party Logistics. The Scientific World Journal, 2012(1), 486306. https://doi.org/10.1100/2012/486306

Buckley, J. J. (1985). Fuzzy hierarchical analysis. Fuzzy sets and systems, 17(3), 233-247. https://doi.org/10.1016/0165-0114(85)90090-9

Gumus, A. T., Yayla, A. Y., Çelik, E., & Yildiz, A. (2013). A combined fuzzy-AHP and fuzzy-GRA methodology for hydrogen energy storage method selection in Turkey. Energies, 6(6), 3017-3032. https://doi.org/10.3390/en6063017

Pamucar, D., Simic, V., Görçün, Ö. F., & Küçükönder, H. (2024). Selection of the best Big Data platform using COBRAC-ARTASI methodology with adaptive standardized intervals. Expert Systems with Applications, 239, 122312.. https://doi.org/10.1016/j.eswa.2023.122312