Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino
<|reference_start|>Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino: A two-wheeled self-balancing robot (TWSBR) is non-linear and unstable system. This study compares the performance of model-based and data-based control strategies for TWSBRs, with an explicit practical educational approach. Model-based control (MBC) algorithms such as Lead-Lag and PID control require a proficient dynamic modeling and mathematical manipulation to drive the linearized equations of motions and develop the appropriate controller. On the other side, data-based control (DBC) methods, like fuzzy control, provide a simpler and quicker approach to designing effective controllers without needing in-depth understanding of the system model. In this paper, the advantages and disadvantages of both MBC and DBC using a TWSBR are illustrated. All controllers were implemented and tested on the OSOYOO self-balancing kit, including an Arduino microcontroller, MPU-6050 sensor, and DC motors. The control law and the user interface are constructed using the LabVIEW-LINX toolkit. A real-time hardware-in-loop experiment validates the results, highlighting controllers that can be implemented on a cost-effective platform.<|reference_end|>
@article{abdelgawad2024model-,
    title={Model- and Data-Based Control of Self-Balancing Robots: Practical
  Educational Approach with LabVIEW and Arduino},
    author={Abdelrahman Abdelgawad, Tarek Shohdy, Ayman Nada},
    journal={arXiv preprint arXiv:2405.03561},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03561},
    primaryClass={cs.RO cs.SY eess.SY}
}