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Model Reference Adaptive Control facilitates nonlinear systems to adapt to modeling errors, environmental changes or structural damage. Most adaptive control frameworks employ Lyapunov analysis in order to establish stability of the closed loop system. However, the derived signal bounds are inherently conservative. Furthermore, these bounds are seldom known; the plant states might still violate structural...
Certification of adaptive control algorithms for use on aerospace applications has not yet been accomplished in the aerospace industry. According to an emerging consensus between various authors, online monitoring and health assessment will play an integral role in closing this gap. In this paper we propose a monitoring system for Model Reference Adaptive Controllers, which enables online detection...
A method based on Bayesian linear regression for output monitoring of an adaptive controller is presented. As a basis, a feedback linearized system is augmented by a Model Reference Adaptive Controller. The application of Bayesian linear regression with online recorded data allows the prediction of the adaptive control output and the detection of off-nominal behavior. Additionally, the presented algorithm...
A hybrid adaptive-optimal control architecture is presented, which is suitable for implementation on systems with fast, nonlinear and uncertain dynamics subject to constraints. Approximate dynamic inversion transforms the nonlinear system into an equivalent linear form. A linear error controller allows a designer chosen reference model to be tracked by the inverted plant. Model Reference Adaptive...
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