The article focuses on developing a TWSBR (Two-Wheeled Self-Balancing Robots) controller aimed at enhancing the performance of underperforming robotic systems, particularly in maintaining stability and precision during movements. It underscores the importance of a novel control approach to address specific performance metrics such as balance, agility, and responsiveness. The paper outlines the establishment of a non-linear system model to capture the intricate dynamics of robotic movements. It briefly discusses the incorporation of non-linearity within the model, potentially involving factors like frictional forces or dynamic load variations. This non-linear framework helps in addressing inherent complexities, thereby improving the performance of two-wheeled robotic systems, especially in scenarios requiring precise balance and regulation. The paper evaluates the performance of various controllers, such as LQR (Linear Quadratic Regulator), ANN (Artificial Neural Network), and PID (Proportional-Integral-Derivative), within the context of the TWSBR system. This analysis provides insights into the effectiveness of different control approaches in improving system stability and precision.