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.

翻译：两轮自平衡机器人(TWSBR)是一个非线性不稳定系统。本研究比较了针对TWSBR的基于模型与基于数据控制策略的性能，并采用了明确的实践性教学方案。基于模型控制(MBC)算法（如Lead-Lag和PID控制）需要熟练的动态建模与数学推导，以获取线性化运动方程并设计相应的控制器。另一方面，基于数据控制(DBC)方法（如模糊控制）提供了一种更简单快捷的途径来设计有效控制器，无需深入理解系统模型。本文通过TWSBR平台阐明了MBC与DBC两种方法的优缺点。所有控制器均在OSOYOO自平衡套件（包含Arduino微控制器、MPU-6050传感器和直流电机）上实现并测试，控制律及用户界面通过LabVIEW-LINX工具包构建。实时硬件在环实验验证了结果，突显了可在低成本平台上实现的控制器方案。