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

TL;DR

This paper compares model-based and data-based control strategies for self-balancing robots, demonstrating their implementation and performance on a cost-effective hardware platform with real-time validation.

Contribution

It provides a practical educational framework for implementing and comparing MBC and DBC methods on a self-balancing robot using LabVIEW and Arduino.

Findings

Data-based control simplifies design process.

Model-based control offers precise dynamic modeling.

Both methods are validated through real-time hardware experiments.

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.