Zahra Yaghoubi, Hassan Zarabadipour


  1. [1] M. H. A. Sidi, K. Hudha, Z. A. Kadir, and N. H. Amer, “Modeling and path tracking control of a tracked mobile robot, ” In 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), pp. 72-76. IEEE, 2018.
  2. [2] H. Xiao, Z. Li, C. Yang, L. Zhang, P. Yuan, L. Ding, and T. Wang, “Robust stabilization of a wheeled mobile robot using model predictive control based on neurodynamics optimization,” IEEE Transactions on Industrial Electronics, vol. 64, no. 1, pp. 505-516, 2017.
  3. [3] F. G. Rossomando, C. Soria, E. O. Freire, and R. O. Carelli, “Sliding mode neuro-adaptive controller designed in discrete time for mobile robots,” Mechatronic Systems and Control (formerly Control and Intelligent Systems), 2018.
  4. [4] F. Debbat, and L. Adouane, “Formation control and role assignment of autonomous mobile robots in unstructured environment,” Control and Intelligent Systems, vol. 44, no. 2, 2016.
  5. [5] M. Korayem, M. Yousefzadeh, and S. Manteghi, “Dynamics and input–output feedback linearization control of a wheeled mobile cable-driven parallel robot,” Multibody System Dynamics, vol. 40, no. 1, pp. 55-73, 2017.
  6. [6] A. Brahmi, M. Saad, G. Gauthier, W.-H. Zhu, and J. Ghommam, “Tracking control of mobile manipulator robot based on adaptive backstepping approach,” International Journal of Digital Signals and Smart Systems, vol. 1, no. 3, pp. 224-238, 2017.
  7. [7] L. Xue, and G. Zhiyong, “Adaptive sliding mode tracking control for nonholonomic wheeled mobile robots with finite time convergence, ” In 2017 36th Chinese Control Conference (CCC), pp. 720-725. IEEE, 2017.
  8. [8] L. Ding, S. Li, H. Gao, C. Chen, and Z. Deng, “Adaptive Partial Reinforcement Learning Neural Network-Based Tracking Control for Wheeled Mobile Robotic Systems,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018.
  9. [9] A. T. Azar, H. H. Ammar, and H. Mliki, “Fuzzy Logic Controller ith Color Vision System Tracking for Mobile Manipulator Robot, ” In International Conference on Advanced Machine Learning Technologies and Applications, pp. 138-146. Springer, Cham, 2018.
  10. [10] L. Ding, S. Li, Y.-J. Liu, H. Gao, C. Chen, and Z. Deng, “Adaptive neural network-based tracking control for full-state constrained wheeled mobile robotic system,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 8, pp. 2410-2419, 2017.
  11. [11] M. Duguleana, and G. Mogan, “Neural networks based reinforcement learning for mobile robots obstacle avoidance,” Expert Systems with Applications, vol. 62, pp. 104-115, 2016.
  12. [12] S. Şahin, and C. Güzeliş, “Chaotification of Real Systems by Dynamic State Feedback,” IEEE Antennas and Propagation Magazine, vol. 52, no. 6, pp. 222-233, 2010.
  13. [13] G. Klančar, D. Matko, and S. Blažič, “A control strategy for platoons of differential drive wheeled mobile robot,” Robotics and Autonomous Systems, vol. 59, no. 2, pp. 57-64, 2011.
  14. [14] S.-h. Kao, C.-c. Yang, C.-d. Shei, and G.-j. Sheu, “Synchronization of Chaotic Gyros via a Novel Adaptive Wrinkling-Type Terminal Sliding Mode Control, ” In ELECTRICAL ENGINEERING AND AUTOMATION: Proceedings of the International Conference on Electrical Engineering and Automation (EEA2016), pp. 1025-1032. 2017.
  15. [15] S. Peng, and W. Shi, “Adaptive Fuzzy Output Feedback Control of a Nonholonomic Wheeled Mobile Robot,” IEEE Access, vol. 6, pp. 43414-43424, 2018.

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