Estimation of immeasurable parameters such as thrust and turbine inlet temperatures in turbine engines constitutes a significant challenge for the aircraft community. A solution to this problem is to estimate these parameters from the measured outputs using an observer. Currently existing technologies rely on Kalman and extended Kalman filters to achieve this estimation. This paper presents an adaptive observer that augments the linear Kalman filter with a neural network to compensate for any nonlinearity that is not handled by the linear filter. The neural network implemented is a Radial Basis Function Network that is trained offline using a growing and pruning algorithm. The adaptive observer is used to estimate HPT inlet temperature, thrust and stall margins.

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