Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916236980387840 |
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| author | Abdelgawad, Abdelrahman Shohdy, Tarek Nada, Ayman |
| author_facet | Abdelgawad, Abdelrahman Shohdy, Tarek Nada, Ayman |
| 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_03561 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino Abdelgawad, Abdelrahman Shohdy, Tarek Nada, Ayman Robotics Systems and Control 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. |
| title | Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2405.03561 |