Model Predictive Control for Rotary Inverted Pendulum: Simulation and Experiment

Authors

  • Phuc-Hoang Huynh Ho Chi Minh City University of Technology and Education
  • Minh-Hanh Nguyen Ho Chi Minh City University of Technology and Education
  • Nguyen-Phat Pham Ho Chi Minh City University of Technology and Education
  • Hoang-Viet-Phuc Duong Ho Chi Minh City University of Technology and Education
  • Huy-Ha Nguyen Ho Chi Minh City University of Technology and Education
  • Duc-Chung Le Ho Chi Minh City University of Technology and Education
  • Minh-Khoa Nguyen Ho Chi Minh City University of Technology and Education
  • Ngoc-Liem Bui Ho Chi Minh City University of Technology and Education
  • Nguyen-Phi-Long Le Ho Chi Minh City University of Technology and Education
  • Van-Dong-Hai Nguyen Ho Chi Minh City University of Technology and Education

DOI:

https://doi.org/10.59247/jfsc.v2i3.263

Keywords:

LQR, MPC, inverted pendulum, STM32F4

Abstract

Rotary Inverted Pendulum (RIP) is one of the simplest nonlinear systems commonly used for validating control algorithms. In this study, two controllers, Model Predictive Control (MPC) and Linear Quadratic Regulation (LQR), are simulated and experimentally validated. These controllers are executed in real-time on a PC, while the STM32F407 chip handles control and data acquisition from the pendulum using a high-speed USB interface. Due to the custom-built nature of this model, there are inaccuracies in the model and parameter identification. However, results show that the MPC controller is better at trajectory tracking and maintaining balance near the set point compared to the LQR controller. On the other hand, the LQR controller responds more robustly to disturbances and external forces, highlighting distinct differences between MPC’s optimization over each prediction horizon and LQR’s single-solution approach for the entire prediction horizon.

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Published

2024-11-02

How to Cite

[1]
P.-H. Huynh, “Model Predictive Control for Rotary Inverted Pendulum: Simulation and Experiment”, JFSC, vol. 2, no. 3, pp. 215–222, Nov. 2024.

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