Filter Design for System Modeling, State Estimation and Fault Diagnosis

Filter Design for System Modeling, State Estimation and Fault Diagnosis analyzes the latest methods in the design of filters for system  modeling, state estimation and fault detection with the intention of  providing a new perspective of both theoretical and practical aspects.
This  book also includes fault diagnosis techniques for unknown but bounded  systems, their real applications on modeling and fault diagnosis for  lithium battery systems, DC-DC converters and spring damping systems. It  proposes new methods based on zonotopic Kalman filtering, a variety of  state estimation methods of zonotope and its derived algorithms, a state  estimation method based on convex space, set inversion interval  observer filtering-based guaranteed fault estimation and a novel  interval observer filtering-based fault diagnosis.
The methods  presented in this text are more practical than the common  probabilistic-based algorithms, since these can be applied in unknown  but bounded noisy environments. This book will be an essential read for  students, scholars and engineering professionals who are interested in  filter design, system modeling, state estimation, fault diagnosis and  related fields.