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

Abdelrahman Abdelgawad, Tarek Shohdy, Ayman Nada

TL;DR

The paper investigates both model-based and data-based control for a two-wheeled self-balancing robot, highlighting nonlinearity and instability as core challenges. It employs Lagrangian dynamics to derive a nonlinear model and linearizes it to a state-space form, then implements and compares PID, lead-lag, and PID-like fuzzy logic controllers on a low-cost OSOYOO platform via LabVIEW-LINX for real-time HIL validation. Results show that model-based controllers achieve superior settling time, steady-state accuracy, and robustness, while the fuzzy logic approach provides a model-free design path with slower convergence and larger overshoot, suggesting tunable trade-offs. The educational framework demonstrates practical steps for teaching mechatronics control, from dynamic modeling and controller design to hardware integration and real-time testing on accessible hardware.

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.

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

TL;DR

The paper investigates both model-based and data-based control for a two-wheeled self-balancing robot, highlighting nonlinearity and instability as core challenges. It employs Lagrangian dynamics to derive a nonlinear model and linearizes it to a state-space form, then implements and compares PID, lead-lag, and PID-like fuzzy logic controllers on a low-cost OSOYOO platform via LabVIEW-LINX for real-time HIL validation. Results show that model-based controllers achieve superior settling time, steady-state accuracy, and robustness, while the fuzzy logic approach provides a model-free design path with slower convergence and larger overshoot, suggesting tunable trade-offs. The educational framework demonstrates practical steps for teaching mechatronics control, from dynamic modeling and controller design to hardware integration and real-time testing on accessible hardware.

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.
Paper Structure (16 sections, 13 equations, 12 figures)

This paper contains 16 sections, 13 equations, 12 figures.

Table of Contents

  1. Introduction
  2. System Modelling
  3. Kinetic Energy
  4. Chassis Kinetic Energy
  5. Wheels Kinetic Energy
  6. Total Kinetic Energy of the robot
  7. Potential Energy and Dissipation Energy
  8. System and Control Implementation
  9. Controller Design
  10. PID Controller Design
  11. Design of Lead-lag Compensator
  12. Fuzzy Logic Controller (FLC)
  13. Results and Discussion
  14. Experimental Validation
  15. Evaluation Metrics
  16. ...and 1 more sections

Figures (12)

  • Figure 1: Kinematics of Robot Motion
  • Figure 2: TWSBR live and CAD representation
  • Figure 3: LabVIEW front panel of PID controlled system
  • Figure 4: Root locus of the uncompensated system
  • Figure 5: Root locus of PID controlled system: $K_p=10$, $K_i=0.005$, and $K_d=0.015$
  • ...and 7 more figures