6 research outputs found
Intelligent, Self-Diagnostic Thermal Protection System for Future Spacecraft
The goal of this project is to provide self-diagnostic capabilities to the thermal protection systems (TPS) of future spacecraft. Self-diagnosis is especially important in thermal protection systems (TPS), where large numbers of parts must survive extreme conditions after weeks or years in space. In-service inspections of these systems are difficult or impossible, yet their reliability must be ensured before atmospheric entry. In fact, TPS represents the greatest risk factor after propulsion for any transatmospheric mission. The concepts and much of the technology would be applicable not only to the Crew Exploration Vehicle (CEV), but also to ablative thermal protection for aerocapture and planetary exploration. Monitoring a thermal protection system on a Shuttle-sized vehicle is a daunting task: there are more than 26,000 components whose integrity must be verified with very low rates of both missed faults and false positives. The large number of monitored components precludes conventional approaches based on centralized data collection over separate wires; a distributed approach is necessary to limit the power, mass, and volume of the health monitoring system. Distributed intelligence with self-diagnosis further improves capability, scalability, robustness, and reliability of the monitoring subsystem. A distributed system of intelligent sensors can provide an assurance of the integrity of the system, diagnosis of faults, and condition-based maintenance, all with provable bounds on errors
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Performance optimization strategies for discrete event and hybrid systems
This work considers the performance optimization of systems involving discrete entities competing for the services provided by a limited number of resources. Such systems are found in transportation, computer and communication networks, and manufacturing, and are typically modeled as discrete event dynamic systems (DEDS), or, more generally, as hybrid dynamical systems (HDS). For DEDS two new performance optimization strategies are proposed. For those whose performance is a function of a controllable parameter vector, an on-line adaptive control scheme is developed. Ile scheme, inspired by classical gain scheduling techniques for nonlinear systems, centers around a lookup table containing the best parameter values to use for different operating conditions. By estimating the instantaneous operating condition and performing a table lookup, the controller is able to adapt to changing operating conditions. The scheme can be used open-loop with a fixed lookup table, or the table can be constructed dynamically for a closed-loop controller. Another DEDS optimization strategy involves the use of calculus of variations (CV) techniques. Except for one other work, CV techniques have not previously been applied to DEDS. Difficulties associated with the nonsmooth ‘ax’ and ‘min’ functions which commonly appear in event-driven dynamics, integer state variables, and uncertainty are addressed. While the first two difficulties can be overcome at least for some problems, CV techniques cannot optimally solve problems involving uncertainty. Nevertheless, the approach supplies insights useful for developing controllers that are robust with respect to the uncertainty. For HDS a new framework combining event-driven dynamics with time-driven dynamics is proposed. The framework which uses “max-plus” recursive equations to describe the event-driven dynamics and differential equations to describe the time-driven dynamics appears useful for modeling many manufacturing systems such as those in steelmaking, food processing, and pharmaceuticals. Optimal control problems trading off demands on the event-driven states against demands on the time-driven states are formulated and simple examples are analyzed using variational techniques. Since the problems generally cannot be solved in closed-form, structural properties of optimal solutions are derived and used to develop quick and efficient numerical algorithms