Model- and Data-Based Control of Self-Balancing Robots: Practical Educational Approach with LabVIEW and Arduino

arXiv - CS - Systems and Control Pub Date : 2024-05-06 DOI:arxiv-2405.03561

Abdelrahman Abdelgawad, Tarek Shohdy, Ayman Nada

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Abstract

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.

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基于模型和数据的自平衡机器人控制：使用 LabVIEW 和 Arduino 的实用教育方法

双轮自平衡机器人（TWSBR）是一个非线性和不稳定的系统。本研究比较了基于模型和基于数据的双轮自平衡机器人控制策略的性能，并采用了明确的实践教育方法。基于模型的控制（MBC）算法，如Lead-Lag和PID控制，需要熟练的动态建模和数学运算来驱动线性化的运动方程并开发适当的控制器。另一方面，基于数据的控制（DBC）方法，如模糊控制，为设计有效的控制器提供了更简单快捷的方法，而无需深入了解系统模型。本文利用 TWSBR 说明了 MBC 和 DBC 的优缺点。所有控制器都是在 OSOYOO 自平衡套件上实现和测试的，包括 Arduino 微控制器、MPU-6050 传感器和直流电机。使用 LabVIEW-LINX 工具包构建了控制法则和用户界面。实时硬件在环实验验证了实验结果，突出了可在高性价比平台上实现的控制器。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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arXiv - CS - Systems and Control

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