"Fault Detection and Isolation: Multi-Vehicle Unmanned System" deals with the design and development of fault detection and isolation algorithms for unmanned vehicles such as spacecraft, aerial drones and other related vehicles. Addressing fault detection and isolation is a key step towards designing autonomous, fault-tolerant cooperative control of networks of unmanned systems. This book proposes a solution based on a geometric approach, and presents new theoretical findings for fault detection and isolation in Markovian jump systems. Also discussed are the effects of large environmental disturbances, as well as communication channels, on unmanned systems. The book proposes novel solutions to difficulties like robustness issues, as well as communication channel anomalies. "Fault Detection and Isolation: Multi-Vehicle Unmanned System" is an ideal book for researchers and engineers working in the fields of fault detection, as well as networks of unmanned vehicles.

"Team Cooperation in a Network of Multi-Vehicle Unmanned Systems" develops a framework for modeling and control of a network of multi-agent unmanned systems in a cooperative manner and with consideration of non-ideal and practical considerations. The main focus of this book is the development of synthesis-based algorithms rather than on conventional analysis-based approaches to the team cooperation, specifically the team consensus problems. The authors provide a set of modified design-based consensus algorithms whose optimality is verified through introduction of performance indices. "

As control systems become more complex and are expected to perform tasks in unknown and extreme environments, they may be subject to various types of faults in their sensors, actuators or other components. It is crucial to be able to diagnose the occurrence of faults and to repair them in order to maintain, guarantee, and improve the overall safety, reliability, and performance of the systems. This book addresses the design challenges of developing and implementing novel integrated fault diagnosis and control technologies for complex linear systems. Integrated Fault Diagnosis and Control Design of Linear Complex Systems considers linear time-invariant (LTI) systems under both time- and event-triggered frameworks. The book initially develops novel methodologies for the problem of integrated fault diagnosis and control of LTI systems to address current design challenges. The results obtained are then extended to a number of complex linear systems, specifically to Markovian jump systems as well as to cooperative multi-agent systems.

Team Cooperation in a Network of Multi-Vehicle Unmanned Systems develops a framework for modeling and control of a network of multi-agent unmanned systems in a cooperative manner and with consideration of non-ideal and practical considerations. The main focus of this book is the development of "synthesis-based" algorithms rather than on conventional "analysis-based" approaches to the team cooperation, specifically the team consensus problems. The authors provide a set of modified "design-based" consensus algorithms whose optimality is verified through introduction of performance indices.

"Fault Detection and Isolation: Multi-Vehicle Unmanned System" deals with the design and development of fault detection and isolation algorithms for unmanned vehicles such as spacecraft, aerial drones and other related vehicles. Addressing fault detection and isolation is a key step towards designing autonomous, fault-tolerant cooperative control of networks of unmanned systems. This book proposes a solution based on a geometric approach, and presents new theoretical findings for fault detection and isolation in Markovian jump systems. Also discussed are the effects of large environmental disturbances, as well as communication channels, on unmanned systems. The book proposes novel solutions to difficulties like robustness issues, as well as communication channel anomalies. "Fault Detection and Isolation: Multi-Vehicle Unmanned System" is an ideal book for researchers and engineers working in the fields of fault detection, as well as networks of unmanned vehicles.

"Neural Network-Based State Estimation of Nonlinear Systems" presents efficient, easy to implement neural network schemes for state estimation, system identification, and fault detection and Isolation with mathematical proof of stability, experimental evaluation, and Robustness against unmolded dynamics, external disturbances, and measurement noises.

Theincreasingcomplexityofspacevehiclessuchassatellites, andthecostreduction measures that have affected satellite operators are increasingly driving the need for more autonomy in satellite diagnostics and control systems. Current methods for detecting and correcting anomalies onboard the spacecraft as well as on the ground are primarily manual and labor intensive, and therefore, tend to be slow. Operators inspect telemetry data to determine the current satellite health. They use various statisticaltechniques andmodels, buttheanalysisandevaluation ofthelargevolume of data still require extensive human intervention and expertise that is prone to error. Furthermore, for spacecraft and most of these satellites, there can be potentially unduly long delays in round-trip communications between the ground station and the satellite. In this context, it is desirable to have onboard fault-diagnosis system that is capable of detecting, isolating, identifying or classifying faults in the system withouttheinvolvementandinterventionofoperators.Towardthisend, theprinciple goal here is to improve the ef?ciency, accuracy, and reliability of the trend analysis and diagnostics techniques through utilization of intelligent-based and hybrid-based methodologi