| Comparative Implementation and Performance Analysis of PID and LQR Control for a Low-Cost Two-Wheel Self-Balancing Robot |
| Paper ID : 1018-EFESCM2025-FULL |
| Authors |
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Mohamed Maher Elmansi *1, omar ahmed mohamed1, Abd Al-Azeez mohamed hesham2, Abd Al-Rahman M Mohsen3 1Department of Mechatronics Engineering, Faculty of Engineering, October 6 University, 6th of October City, 12585, Giza, Egypt. 26th of October City, Giza Governorate 31 Department of Mechatronics Engineering, Faculty of Engineering, October 6 University, 6th of October City, 12585, Giza, Egypt. |
| Abstract |
| This research presents the design, development, and control of a two-wheel, self-balancing robot built using low-cost, easily accessible components. The robot utilizes two DC motors equipped with rotary encoders for motion feedback, an MPU-6050 inertial measurement unit (IMU) for tilt sensing, and an Arduino Uno microcontroller for onboard processing and control. The primary objective of the study is to achieve stable upright balancing of the robot through the implementation of two widely used control strategies: Proportional-Integral-Derivative (PID) and Linear Quadratic Regulator (LQR). The study explores each controller’s design, modeling, and tuning process in detail, highlighting the challenges and trade-offs involved in adapting them to a constrained embedded system. The PID controller is tuned using heuristic and empirical methods, while the LQR controller is designed based on a linearized state-space model of the system. A key contribution of this research lies in the comparative analysis of both controllers in terms of stability, response time, robustness, and computational efficiency. Experimental tests and simulations are conducted to evaluate the real-world performance of each approach under various operating conditions. The results demonstrate the feasibility of implementing advanced control algorithms on low-cost platforms and provide valuable insights into selecting and tuning appropriate controllers for self-balancing applications. This work serves as a practical reference for students, researchers, and hobbyists interested in control systems, robotics, and embedded system design. |
| Keywords |
| self-balancing robot, LQR, Kalman filter, Low cost controller |
| Status: Accepted |
