Intelligent Control of Differential Drive Robots Subject to Unmodeled Dynamics with EKF-based State Estimation

Differential drive robots operating in uncertain environments face significant control and state estimation challenges due to unmodeled dynamics and sensor measurement degradation. Researchers have developed a unified framework combining a Lyapunov-based nonlinear controller and adaptive neural networks to address these issues¹. This approach enables reliable control and state estimation, even when system dynamics are partially unknown. The framework utilizes an Extended Kalman Filter (EKF) for state estimation, allowing the robot to adapt to changing conditions. By integrating these components, the framework provides a robust solution for differential drive robots operating in dynamic environments. The ability to accurately estimate and control robot states is critical for safe and efficient operation, making this research significant for applications where robots interact with uncertain or unstructured environments. This development matters to robotics practitioners because it enhances the reliability and autonomy of differential drive robots, enabling them to operate effectively in a wide range of scenarios.