Trajectory Tracking of a Two-Wheeled Mobile Robot Using Backstep** and Nonlinear PID Controller

  • Conference paper
  • First Online:
Artificial Intelligence and Digitalization for Sustainable Development (ICAST 2022)

Abstract

Many researchers have become interested in wheeled mobile robot (WMR) trajectory tracking control in recent years. This is due to the increased application of mobile robots in the industry, the military, the home, and public service. Classically, the movement of WMR is controlled depending on its kinematic model. However, in real-time applications, both the dynamic and kinematic models of robots and external disturbance and uncertainty affect system performance. This paper proposes backstep** combined with a Nonlinear Proportional-Integral-Derivative (NPID) controller to control a two-wheeled mobile robot (TWMR). The kinematic and dynamic models of the WMR are derived. The dynamic modeling is derived using a Lagrangian approach, and stability of the system is achieved using the Lyapunov method. The controller gains are optimized using the Genetic Algorithm optimization technique. The proposed algorithms’ performance is tested using Matlab software. The simulation result shows that the proposed method achieved preferable reference trajectory tracking with a minimum tracking error. The proposed controller outperforms the GA-based backstep** plus PID controller in terms of root-mean-square (RMS) of trajectory tracking error (47.36% in a linear and 60.32% in a nonlinear case). In addition, it shows good unknown disturbance rejection and initial point change in all scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free ship** worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Fierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot using neural networks. IEEE Trans. Neural Netw. 9(4), 589–600 (1998). https://doi.org/10.1109/72.701173

    Article  Google Scholar 

  2. Uddin, N.: Trajectory tracking control system design for autonomous two-wheeled robot. JURNAL INFOTEL 10(3), 90 (2018). https://doi.org/10.20895/infotel.v10i3.393

    Article  Google Scholar 

  3. Ren, C., Ji, J.-H., Yan, H.-Y., Zhang, H., Yue, J.-Z.: A backstep** control method for mobile robot path tracking. In: Proceedings of the 3rd Annual International Conference on Mechanics and Mechanical Engineering (MME 2016), vol. 105 (2017). https://doi.org/10.2991/mme-16.2017.94

  4. Chang, H., **, T.: Adaptive tracking controller based on the pid for mobile robot path tracking. In: Lee, J., Lee, M.C., Liu, H., Ryu, J.-H. (eds.) ICIRA 2013. LNCS (LNAI), vol. 8102, pp. 540–549. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40852-6_55

    Chapter  Google Scholar 

  5. Moqbel Obaid, M.A., Husain, A.R., Mohammed Al-kubati, A.A.: Robust backstep** tracking control of mobile robot based on nonlinear disturbance observer. Int. J. Electr. Comput. Eng. (IJECE) 6(2), 901 (2016). https://doi.org/10.11591/ijece.v6i2.9594

    Article  Google Scholar 

  6. Xu, Q., Kan, J., Chen, S., Yan, S.: Fuzzy PID based trajectory tracking control of mobile robot and its simulation in simulink. Int. J. Control Autom. 7(8), 233–244 (2014). https://doi.org/10.14257/ijca.2014.7.8.20

    Article  Google Scholar 

  7. Fierro, R., Lewis, F.L.: Control of a nonholomic mobile robot: backstep** kinematics into dynamics. J. Robot. Syst. 14(3), 149–163 (1997). https://doi.org/10.1002/(SICI)1097-4563(199703)14:3%3c149::AID-ROB1%3e3.3.CO;2-N

    Article  MATH  Google Scholar 

  8. Fierro, R., Lewis, F.L.: Control of a nonholonomic mobile robot: backstep** kinematics into dynamics. In: Proceedings of 1995 34th IEEE Conference on Decision and Control, vol. 4, no. December, pp. 3805–3810 (1995). https://doi.org/10.1109/CDC.1995.479190

  9. Hassani, I., Maalej, I., Rekik, C.: Backstep** tracking control for nonholonomic mobile robot. In: 2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), pp. 63–68 (2020). https://doi.org/10.1109/IC_ASET49463.2020.9318221

  10. Dagher, K., Al-araji, A.: Design of a nonlinear PID neural trajectory tracking controller for mobile robot based on optimization algorithm. Eng. Tech J. 32(4), 973–985 (2014)

    Google Scholar 

  11. Zangina, U., Buyamin, S., Abidin, M.S.Z., Mahmud, M.S.A., Hasan, H.S.: Nonlinear PID controller for trajectory tracking of a differential drive mobile robot. J. Mech. Eng. Res. Dev. 43(7), 255–269 (2020). http://eprints.utm.my/id/eprint/90651/1/UmarZangina2020_NonLinearPIDControllerforTrajectoryTracking.pdf

  12. Kalyoncu, M., Demirbaş, F.: Differential drive mobile robot trajectory tracking with using PID and kinematic based backstep** controller. Selcuk Univ. J. Eng. Sci. Technol. 5(1), 1–15 (2017). https://doi.org/10.15317/Scitech.2017.65

  13. Yousuf, B.M., Saboor Khan, A., Munir Khan, S.: Dynamic modeling and tracking for nonholonomic mobile robot using PID and backstep**. Adv. Control Appl. 3(3), 1–12 (2021). https://doi.org/10.1002/adc2.71

    Article  Google Scholar 

  14. Ben Jabeur, C., Seddik, H.: Design of a PID optimized neural networks and PD fuzzy logic controllers for a two-wheeled mobile robot. Asian J. Control 23(1), 23–41 (2021). https://doi.org/10.1002/asjc.2356

    Article  Google Scholar 

  15. Mohareri, O., Dhaouadi, R., Rad, A.B.: Indirect adaptive tracking control of a nonholonomic mobile robot via neural networks. Neurocomputing 88, 54–66 (2012). https://doi.org/10.1016/j.neucom.2011.06.035

    Article  Google Scholar 

  16. Ahmad Abu Hatab, R.D.: Dynamic modelling of differential-drive mobile robots using Lagrange and newton-Euler methodologies: a unified framework. Adv. Robot. Autom. 02(02) (2013). https://doi.org/10.4172/2168-9695.1000107

  17. Benchouche, W., Mellah, R., Bennouna, M.S.: The Impact of the dynamic model in feedback linearization trajectory tracking of a mobile robot. Period. Polytech. Electr. Eng. Comput. Sci. 65(4), 329–343 (2021). https://doi.org/10.3311/PPee.17127

    Article  Google Scholar 

  18. .Vaidyanathan, A.T.A.S.: Backstep** Control of Nonlinear Dynamical Systems. Elsevier (2021)

    Google Scholar 

  19. **, G.-G., Son, Y.-D.: Design of a nonlinear PID controller and tuning rules for first-order plus time delay models. Stud. Inform. Control 28(2), 157–166 (2019). https://doi.org/10.24846/v28i2y201904

    Article  Google Scholar 

  20. Messom, C.: Genetic algorithms for auto-tuning mobile robot motion control. Res. Lett. Inf. Math. Sci. 3(2002), 129–134 (2002). http://www.massey.ac.nz/~wwiims/research/letters/

  21. Martins, N.A., Bertol, D.W.: Wheeled Mobile Robot Control, vol. 380. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-77912-2

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lencho Duguma Fufa .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Fufa, L.D., Ayenew, E. (2023). Trajectory Tracking of a Two-Wheeled Mobile Robot Using Backstep** and Nonlinear PID Controller. In: Woldegiorgis, B.H., Mequanint, K., Bitew, M.A., Beza, T.B., Yibre, A.M. (eds) Artificial Intelligence and Digitalization for Sustainable Development. ICAST 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 455. Springer, Cham. https://doi.org/10.1007/978-3-031-28725-1_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-28725-1_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-28724-4

  • Online ISBN: 978-3-031-28725-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Navigation