| Machinery prognostic and health management (PHM) technologies are becoming increasingly widespread. PHM systems may be tailored to specific goals from high-speed fault detection and isolation in electrical power systems, wherein PHM systems act on microsecond time-scales, to rapid fault detection and accommodation in aircraft for improved vehicle safety, wherein PHM systems act on one second time scales, to condition-based maintenance (CBM) goals wherein PHM systems act on time scales of minutes, hours, or even tens of hours of vehicle usage.;The overall objective of this dissertation research is to improve the affordability, survivability, and service life of next generation vehicles and systems by providing improved adaptive diagnostics and control. These algorithms utilize on-board sensors and data processing to achieve a low-cost, real-time autonomous health monitoring and adaptive control solution.;The shortcoming of many traditional model-based PHM approaches is that, in practice, the behavior of many physical systems is complex. Manufacturing tolerances and environmental impacts may cause variations in unfaulted system dynamics between nominally identical plants. Exhaustive data collection to develop highly accurate a priori models of unfaulted system behavior may be costly and impractical in many applications. It would be highly valuable to develop an automated, readily portable set of integrated adaptive diagnostic and control algorithms for a large class of dynamic systems.;This research contributes to the field of adaptive diagnostics and control---leveraging proven adaptive control structures for improved adaptive diagnostics and then exploiting the improved diagnostic information to improve adaptive controller performance. Advances are developed for uncertain linear and nonlinear systems with and without full state measurement that are subject to actuator, sensor, and plant faults. |
