Trajectory Tracking Using Neuro-Fuzzy Controller

References

[1] Abdi, H., et al. (2018). Adaptive neural network control for trajectory tracking of nonholonomic mobile robots. Robotics and Autonomous Systems, 105, 148-159.

[2] Babic, Z., et al. (2019). Adaptive trajectory tracking control of mobile robots with parametric uncertainties. Robotics and Autonomous Systems, 120, 103259.

[3] Bhattacharjee, A., et al. (2019). Path planning and tracking control of mobile robot using A* algorithm. 2019 3rd International Conference on Chen, C., et al. (2018). Adaptive neural network control for trajectory tracking of mobile robots. Robotics and Autonomous Systems, 105, 148-159.

[4] Chen, C., et al. (2020). Adaptive fuzzy sliding mode control for trajectory tracking of mobile robots. Robotics and Autonomous Systems, 123, 103391.

[5] Chen, Z., et al. (2016). Design of mobile robot trajectory tracking control based on improved PID algorithm. Journal of Physics: Conference Series, 744(1), 012092.

[6] Elhassouny, A.M., et al. (2018). Fuzzy logicbased sensor fusion for obstacle detection and avoidance of mobile robot. International Journal of Robotics and Automation, 33(2), 135-143.

[7] Hernández-González, M., et al. (2020). Model predictive control for trajectory tracking in nonholonomic mobile robots. Robotics and Autonomous Systems, 125, 103369.

[8] Huang, Q., et al. (2019). Real-time monocular vision-based path planning and tracking for mobile robots in indoor environments. Sensors, 19(6), 1382.

[9] ui, C., & Leung, F. H. F. (2008). Adaptive neuro-fuzzy inference system for mobile robot trajectory tracking control. Robotics and Autonomous Systems, 56(4), 324-333. Jang, J. S. R. (1993). ANFIS: Adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man and Cybernetics, 23(3), 665-685.

[10] Karaman, S., & Frazzoli, E. (2011). Sampling-based algorithms for optimal motion planning. The International Journal of Robotics Research, 30(7), 846-894.

[11] Khamis, N. K., & El-Sayed, H. M. (2020). Comparative study of PID, fuzzy and ANFIS controllers for mobile robot trajectory tracking. Computers, Materials & Continua, 62(2), 1043-1061.

[12] Kim, K. H., et al. (2019). Trajectory tracking control of differential-drive mobile robots using adaptive neuro-fuzzy inference system. Applied Sciences, 9(12), 2487.

[13] Lee, J.H., et al. (2019). PID control-based trajectory tracking for autonomous mobile robot with rear-wheel steering. Electronics, 8(4), 379.

[14] Li, C., et al. (2020). Adaptive fuzzy control of mobile robots based on sliding mode control. IEEE Access, 8, 205714-205721.

[15] Li, X., Zhao, J., & Zhang, Y. (2016). Adaptive trajectory tracking control of mobile robots using ANFIS. IEEE Transactions on Industrial Electronics, 63(4), 2357-2367. Lin, S., et al. (2017). Particle swarm optimization for trajectory tracking control of autonomous mobile robots. Applied Sciences, 7(5), 446.

[16] M. W. Spong, S. Hutchinson and M. Vidyasagar, "Robot Modeling and Control," John Wiley & Sons, 2005. -

[17] Montano, J., et al. (2020). Localization of a mobile robot through particle filtering in an indoor environment. Sensors, 20(3), 841.

[18] Nguyen, T. T., et al. (2017). Trajectory tracking control of mobile robot using adaptive neuro-fuzzy inference system. In 2017 International Conference on System Science and Engineering (ICSSE) (pp. 150-155). IEEE.

[19] Samanta, S., et al. (2021). Adaptive trajectory tracking control of wheeled mobile robot using ANFIS. Journal of Intelligent & Robotic Systems, 101(2), 27-42.

[20] Tuna, C., et al. (2018). Mobile robot path tracking using PID control with reinforcement learning. 2018 International Conference on Computer Science and Engineering (UBMK).

[21] Wang, Z., et al. (2020). Trajectory tracking control of mobile robot based on ANFIS and obstacle avoidance. In 2020 Chinese Automation Congress (CAC) (pp. 3163- 3168). IEEE.

[22] Zhang, G., Liu, Y., & Li, W. (2017). Comparative analysis of trajectory tracking control methods for mobile robots. Robotics and Computer-Integrated Manufacturing, 44, 180- 191.

[23] Zhang, J., et al. (2017). Trajectory planning and control of mobile robots in dynamic environments. Robotics and Autonomous Systems, 97, 11-22.

[24] Zhang, Q., & Sugeno, M. (2013). Adaptive fuzzy systems. In Industrial applications of fuzzy technology (pp. 135-157). Springer.

@article{1706800,
      author = {Emmanuel Unanaowo Eno},
      title = {Trajectory Tracking Using Neuro-Fuzzy Controller},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {8},
      number = {6},
      pages = {851-861},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1706800.pdf},
      abstract = {This dissertation focuses on trajectory tracking for a 4 degree-of-freedom (4-DOF) robot arm manipulator, harnessing the capabilities of neuro-fuzzy controller. The research journey begins by exploring various tracking models; including kinematics, torques and velocities. Subsequently, it narrows its focus to trajectory tracking with torques, leading to the implementation of the controller. This implementation undergoes an extensive learning process that includes identifying and refining fuzzy inference system (FIS) parameters. This critical phase makes use of a training dataset derived from a finely tuned PID model that incorporates H-infinity control techniques. Once trained, the neuro-fuzzy controller equips the robot with the essential knowledge and adaptability necessary for precise trajectory tracking. Through a series of meticulous simulations, the study effectively displays the controller's prowess in accurately navigating and adhering to predefined reference trajectories. Nevertheless, the practical application of robotics often introduces physical disturbances and uncertainties. In response to these challenges, the dissertation underscores the imperative need for a controller capable of effectively mitigating these disruptions. The proposed neuro-fuzzy controller emerges as a versatile solution, demonstrating its adaptability to a wide array of predefined reference trajectories and highlighting robust performance, even in the presence of disturbances. The significance of this research lies in its substantial contributions towards the field of robotics. It introduces an advanced trajectory-tracking solution that significantly enhances accuracy and adaptability, effectively bridging the gap between theoretical models and practical robotic control. Furthermore, the dissertation suggests the incorporation of optimization techniques further elevates the controller's performance, rendering it a valuable tool for addressing dynamic real-world challenges. In terms of prospects, this research proposes the integration of machine learning techniques to bolster adaptability and the exploration of optimization algorithms for precise parameter tuning. Additionally, it suggests the evaluation of the controller's performance across diverse robot types, expanding its applicability throughout the diverse landscape of the robotics industry. In summation, this dissertation heralds a new era in the trajectory tracking arena, presenting a versatile and robust control solution with the potential to revolutionize robotics applications.},
      keywords = {Robot, Neoro-Fuzzy, Trajectory Tracking, Autonomous, Controller Design},
      month = {December},
  }