Trajectory Tracking using LQR Control for Pendubot: Simulation and Experiment

Authors

  • Trong-Bang Tran Viet Solution Technology Technical Company Limited
  • Hoang-Thien Nguyen Ho Chi Minh City University of Technology and Education
  • Tay Nguyen Ho Chi Minh City University of Technology and Education
  • Duc-Dat Dang Ho Chi Minh City University of Technology and Education
  • Duong-Minh-Quang Pham Ho Chi Minh City University of Technology and Education
  • Nhat-Duy Le Ho Chi Minh City University of Technology and Education
  • Hoang-Khuong Huynh Ho Chi Minh City University of Technology and Education
  • Thanh-Quoc-Du Phan Ho Chi Minh City University of Technology and Education
  • Bao-Huy Nguyen Ho Chi Minh City University of Technology and Education
  • Ngo-Huu-Tung Nguyen Ho Chi Minh City University of Technology and Education
  • Le-Quoc-Toan Pham Ho Chi Minh City University of Technology and Education
  • Trung-Hieu Nguyen Ho Chi Minh City University of Technology and Education
  • Quang-Vinh Dang Ho Chi Minh City University of Technology and EducationC

DOI:

https://doi.org/10.59247/jfsc.v2i1.163

Keywords:

Pendubot, LQR Control, SIMO System, Stabilize Control, Genetic Algorithm

Abstract

Pendubot, a unique single-input-multiple-output (SIMO) system, is commonly employed in laboratories to validate control algorithms. In this article, we develop an LQR controller to simulate and assess its effectiveness on this model. Specifically targeting the TOP position for control, we not only verify the controller's quality but also ensure the motion system accurately tracks a predefined trajectory, encompassing sine and square pulses. Control parameters are meticulously chosen through a genetic algorithm (GA). Although LQR is not highly rated for trajectory tracking due to its relatively small operational range, our successful simulations and control of this system are attributed to the assistance of GA

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Published

2024-03-01

How to Cite

[1]
T.-B. Tran, “Trajectory Tracking using LQR Control for Pendubot: Simulation and Experiment”, J Fuzzy Syst Control, vol. 2, no. 1, pp. 18–21, Mar. 2024.

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