Advancements and prospects of fuzzy-based adaptive unscented Kalman filters for nonlinear systems: A review

Rapid developments in computational technologies have recently imposed even more significant requirements for efficient and accurate state estimation methods that can be applied to nonlinear dynamic systems. Among the widely known nonlinear estimation techniques stands the unscented Kalman filter. However, in real-life applications, its performance is usually affected due to the presence of noise and model uncertainties. Such disturbances are handled by adaptation-based approaches, wherein the noise covariances are adjusted. Many researchers have been attracted to adaptive methods using fuzzy tuning with covariance matching in the last decade. Adaptation methodologies based on fuzzy logic applied to an unscented Kalman filter related to different practical applications are reviewed herein. It is performed by examining various kinds of fuzzy inference systems, other categories of membership functions, adaptation laws, or tuning relations of covariance matrices and their respective applications. Fuzzy logic control is one of the parts or components of artificial intelligence. The fuzzy inference systems, such as Mamdani and Takagi-Sugeno, implemented for adaptive estimation techniques with unscented Kalman filters in real-world applications are highlighted. Furthermore, the readers may easily refer to the highlighted future possibilities and significant challenges in the field.