Observer Design

Observer design focuses on estimating the unmeasurable internal states of a system using available measurements, aiming to improve system control and understanding. Current research emphasizes developing robust observers for nonlinear systems, employing techniques like Kalman filtering (extended and invariant versions), neural networks, and meta-learning to handle complexities such as noise, uncertainties, and model inaccuracies. These advancements are crucial for applications ranging from robotics and control systems to medical imaging, where accurate state estimation is essential for optimal performance and decision-making. The field is increasingly incorporating data-driven approaches and rigorous performance evaluations, such as observer studies, to ensure the reliability and effectiveness of observer designs.