PERFORMANCE ANALYSIS OF BACKSTEPPING AND OPTIMIZED FOPID CONTROLLER FOR TRAJECTORY TRACKING OF WHEELED MOBILE ROBOT
1. Aregawi Weldemariam, Addis Ababa science and technology university, Student, Ethiopia
Differential drive mobile robots are now used for a wider range of commercial and industrial
applications than just for scientific study. The field of mobile robotics faces a serious challenge
with trajectory tracking. The trajectory tracking of the differential-drive wheeled mobile robot is
studied in this thesis using a control strategy that combines kinematics based backstepping with
optimized fractional-order PID controllers for the dynamics. Moreover, to obtain an optimal control
system, fuzzy inference system and genetic algorithm optimization techniques are applied
separately to tune the parameters of the fractional order PID dynamic controllers. Finally, several
simulations are implemented to the trajectory tracking of mobile robots in the cases with and
without disturbance signal, and the results can confirm the effectiveness and superiority of the
combined control scheme. The fuzzy based FOPID controller response performs better with ITAE
of 0.0289 and 0.0364 in the x and y direction respectively whereas with GA based FOPID the
ITAE is 0.374 and 0.285. the robustness of the backstepping controller is also clearly proved.
Wheeled Mobile Robot Backstepping controller FOPID Genetic algorithm Fuzzy Inference system trajectory tracking Wheeled Mobile Robot Backstepping controller FOPID Genetic algorithm Fuzzy Inference system trajectory tracking
The kinematic-based backstepping controller
works excellently at following the desired
10
trajectory with the minimum amount of error, and
it exhibits great disturbance rejection (robustness)
performance, which the proposed dynamic
controllers are not good at.
As can be observed from the simulation results,
kinematic-based backstepping controllers with
fuzzy inference-optimized FOPID controllers
offer higher performance characteristics in terms
of trajectory error and robustness against
perturbation than the other strategies covered in
this work. In order to verify simulation results and
conduct real-time implementation, it is advised to
carry out practical implementation of these
controllers for trajectory tracking control of the
wheeled mobile robot. This is due to the
possibility that there could be modelling
inaccuracies when creating the robot, meaning
that a strategy that worked best in the model could
not always perform effectively in reality.
Furthermore, the established control strategy
and optimization method may be applied to a
variety of applications, including robot
manipulators, unmanned aerial vehicles, and
others, by simply altering the plant system.
1. [1] G. Klancar, A. Zdesar, S. Blazic, and I. Skrjanc, Wheeled mobile robotics: from fundamentals towards autonomous systems. Butterworth-Heinemann, 2017. [2] A. Mahdavi and M. Carvalho, "Optimal trajectory and schedule planning for autonomous guided vehicles in flexible manufacturing system," in 2018 Second IEEE International Conference on Robotic Computing (IRC), 2018: IEEE, pp. 167-172. [3] M. P. Lazarević, P. D. Mandić, B. Cvetković, L. Bučanović, and M. Dragović, "Advanced open-closed-loop PIDD 2/PID type ILC control of a robot arm," in 2018 Innovations in Intelligent Systems and Applications (INISTA), 2018: IEEE, pp. 1-8. [4] K. M. Passino, "Intelligent control: an overview of techniques," ed: Citeseer, 2001, pp. 104-133. [5] T. P. Nascimento, C. E. Dórea, and L. M. G. Gonçalves, "Nonholonomic mobile robots' trajectory tracking model predictive control: a survey," Robotica, vol. 36, no. 5, pp. 676-696, 2018. [6] A. Pandey, S. Jha, and D. Chakravarty, "Modeling and control of an autonomous three wheeled mobile robot with front steer," in 2017 First IEEE International Conference on Robotic Computing (IRC), 2017: IEEE, pp. 136-142. [7] U. Zangina, S. Buyamin, M. S. Z. Abidin, M. S. Azimi, and H. Hasan, "Non-linear PID controller for trajectory tracking of a differential drive mobile robot," Journal of Mechanical Engineering Research and Developments, vol. 43, no. 1, pp. 255-270, 2020. [8] B. Moudoud, H. Aissaoui, and M. Diany, "Robust Adaptive Trajectory Tracking Control Based on Sliding Mode of Electrical Wheeled Mobile Robot," International Journal of Mechanical Engineering and Robotics Research, vol. 10, no. 9, 2021. [9] I. Hassani, I. Maalej, and C. Rekik, "Backstepping tracking control for nonholonomic mobile robot," in 2020 4th International Conference on Advanced Systems and Emergent Technologies (IC_ASET), 2020: IEEE, pp. 63-68. 11 [10] M. Saeedi, J. Zarei, R. Razavi-Far, and M. Saif, "Adaptive Sliding Mode Fuzzy PID Control: Supervisory Control Systems," in 2020 IEEE International Systems Conference (SysCon), 2020: IEEE, pp. 1-6. [11] J.-y. Zhai and Z.-b. Song, "Adaptive sliding mode trajectory tracking control for wheeled mobile robots," International Journal of Control, vol. 92, no. 10, pp. 2255-2262, 2019. [12] A. Azzabi and K. Nouri, "Design of a robust tracking controller for a nonholonomic mobile robot based on sliding mode with adaptive gain," International journal of advanced robotic systems, vol. 18, no. 1, p. 1729881420987082, 2021. [13] S. Jaiswal, C. S. Kumar, M. M. Seepana, and G. U. B. Babu, "Design of fractional order PID controller using genetic algorithm optimization technique for nonlinear system," Chemical Product and Process Modeling, vol. 15, no. 2, 2020. [14] F. A. Hasan and L. J. Rashad, "Fractionalorder PID controller for permanent magnet DC motor based on PSO algorithm," International Journal of Power Electronics and Drive Systems, vol. 10, no. 4, p. 1724, 2019. [15] J.-B. M. Zanga, B. M. Lonla, A. Nanfak, and G. M. Ngaleu, "Fuzzy-FOPID control for tracking the trajectory of nonholonomic Wheeled Mobile Robot," Journal of Electrical Engineering, Electronics, Control and Computer Science, vol. 8, no. 2, pp. 29-38, 2021. [16] L. Xu, J. Du, B. Song, and M. Cao, "A combined backstepping and fractional-order PID controller to trajectory tracking of mobile robots," Systems Science & Control Engineering, vol. 10, no. 1, pp. 134-141, 2022. [17] F. N. Martins, M. Sarcinelli-Filho, and R. Carelli, "A velocity-based dynamic model and its properties for differential drive mobile robots," Journal of intelligent & robotic systems, vol. 85, no. 2, pp. 277-292, 2017. [18] A. Brahmi, M. Saad, B. Brahmi, I. E. Bojairami, G. Gauthier, and J. Ghommam, "Robust adaptive tracking control for uncertain nonholonomic mobile manipulator," Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering, vol. 236, no. 2, pp. 395- 405, 2022. [19] O. Mohareri, "Mobile robot trajectory tracking using neural networks," 2009. [20] J. Amaral, R. Tanscheit, and M. Pacheco, "Tuning PID controllers through genetic algorithms," complex systems, vol. 2, no. 3, 2018. [21] A. Jayachitra and R. Vinodha, "Genetic Algorithm Based PID Controller Tuning Approach for Continuous Stirred Tank Reactor," Advances in Artificial Intelligence (16877470), 2014. [22] G. Purohit, A. M. Sherry, and M. Saraswat, "Optimization of function by using a new MATLAB based genetic algorithm procedure," International journal of computer applications, vol. 61, no. 15, 2013.
1. Conceptualization: Aregawi Hailu and Lebsework Negash designed the research framework and developed the research questions. 2. Methodology: Aregawi Hailu designed the methodology and conducted the experiments. 3. Formal Analysis: Aregawi Hailu performed the data analysis. 4. Investigation: Aregawi Hailu conducted the investigation. 5. Resources: Aregawi Hailu and Lebsework Negash provided the necessary resources and materials for the research. 6. Data Curation: Aregawi Hailu curated and managed the data. 7. Writing – Original Draft: Aregawi Hailu prepared the initial draft of the manuscript. 8. Writing – Review & Editing: Lebsework Negash reviewed and edited the manuscript for intellectual content. 9. Visualization: Aregawi Hailu created the visual representations of data (figures, tables, etc.). 10. Supervision: Lebsework Negash supervised the overall research project. All authors have read and agreed to the published version of the manuscript.
This research was funded by Addis Ababa science and technology university. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
MATLAB is the main software tool we used.
No conflict of Interest
We want to express a special thanks to all of family and friends for their unwavering support and encouragement during this effort.
The data that support the findings of this study are
available from the corresponding author upon
reasonable request.