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Application Of LQR and Fuzzy-LQR Algorithms for Controlling Self-Balancing Bike Model

by Vu Nguyen Tran Long, Phuc Vo Hoang, Thai-Hoang Huynh
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 2
Year of Publication: 2025
Authors: Vu Nguyen Tran Long, Phuc Vo Hoang, Thai-Hoang Huynh
10.5120/ijca2025924787

Vu Nguyen Tran Long, Phuc Vo Hoang, Thai-Hoang Huynh . Application Of LQR and Fuzzy-LQR Algorithms for Controlling Self-Balancing Bike Model. International Journal of Computer Applications. 187, 2 ( May 2025), 34-41. DOI=10.5120/ijca2025924787

@article{ 10.5120/ijca2025924787,
author = { Vu Nguyen Tran Long, Phuc Vo Hoang, Thai-Hoang Huynh },
title = { Application Of LQR and Fuzzy-LQR Algorithms for Controlling Self-Balancing Bike Model },
journal = { International Journal of Computer Applications },
issue_date = { May 2025 },
volume = { 187 },
number = { 2 },
month = { May },
year = { 2025 },
issn = { 0975-8887 },
pages = { 34-41 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number2/application-of-lqr-and-fuzzy-lqr-algorithms-for-controlling-self-balancing-bike-model/ },
doi = { 10.5120/ijca2025924787 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-05-17T02:45:32+05:30
%A Vu Nguyen Tran Long
%A Phuc Vo Hoang
%A Thai-Hoang Huynh
%T Application Of LQR and Fuzzy-LQR Algorithms for Controlling Self-Balancing Bike Model
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 2
%P 34-41
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The self-balancing problem is crucial for the future development of self-driving technology, particularly for two-wheeled vehicles. This report investigates and analyzes a self-balancing bike model using a control system based on reaction wheel actuation. The Linear Quadratic Regulator (LQR) and fuzzy controller combined with LQR (Fuzzy-LQR) are applied to stabilize the bike by adjusting the reaction wheel’s response. To ensure a comprehensive approach to model development, following a structured methodology is necessary: theoretical analysis, data collection, mathematical modeling and simulation, and real-world experimentation. The results demonstrate that both control methods can effectively stabilize the system. However, balancing performance and energy efficiency must be carefully considered for real-world applications. The Fuzzy-LQR approach performs better than the standalone LQR method, highlighting the advantages of integrating human-inspired intelligent control with traditional control techniques. This finding reinforces the potential of hybrid control strategies in handling nonlinear self-balancing bike models in practical applications.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Self-balancing bike LQR Fuzzy–LQR

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