Adaptive Observer

Adaptive observers are systems designed to estimate the internal state of a dynamic system, even in the presence of uncertainties or disturbances. Current research focuses on developing robust adaptive observers for diverse applications, employing techniques such as nonlinear deterministic observers, hybrid approaches combining model-based estimation with neural networks, and Luenberger-type observers coupled with novel parameter estimation methods (e.g., attention-based averaging). These advancements are crucial for improving the performance of various systems, including autonomous vehicles (e.g., accurate side-slip angle estimation), robotic manipulators (e.g., sensor fault detection and compensation), and navigation systems operating in GPS-denied environments. The resulting improved state estimation enhances control precision, fault tolerance, and overall system reliability.