Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2405.03561 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Table of Contents:
- 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.