Neural Nets and Adaptive Reconfigurable Spacecraft Guidance and Control under Failure Conditions
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AbstractFuture NASA space missions call for unprecedented levels of precision and reliability. Space Structures like other engineering systems, will suffer from unexpected failures and environmental disturbances. Although a large number of control subsystems are used to nullify or ameliorate the effects of such events, a health monitoring and supervisory control system is required to constantly monitor the operating status of each of these subsystems. Whenever a failure is predicted, the supervisor will take corrective actions. A new technique is proposed here for health monitoring and supervisory control which combines techniques from the areas of Artificial Neural Nets (ANN) and Robust Stochastic Control Theory. The proposed Phase I effort will consist of the following tasks: (i) Identify and develop an architecture for the health monitoring and supervisory control system, (ii) Identify and develop techniques for detection and classification using ANN after preprocessing by Extended Kalman Filtering and Principal Component Analysis, (iii) Identify and develop techniques for adaptive reconfiguration of control under failure conditions, and (iv) Demonstrate the detection, classification and reconfiguration for a sensor/actuator failure on a NASA Test Article case study simulation. During Phase II, other types of failures will be considered and the software will be implemented on line and tested on a NASA Test Article.
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