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.

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