Application of the Kalman filtering technique for nonlinear state estimation in propulsion system

The estimation of the propulsion system states and especially of the main engine is essential for control, diagnosis and performance evaluation. If all the required sensors were available, providing required measurements, the state and performance monitoring is of no particular difficulty. However, not all the required parameters can be measured directly, or the addition of multiple measurement channels is out of appropriateness. Furthermore, the propulsion plant state dynamics is justified by propeller load torque fluctuation that in turn is caused by fluctuating effective inflow velocity into the propeller, and which cannot be measured directly. Thus, the problem of estimating unmeasured state and disturbance variables of the propulsion system is considered and formulated as the design of an unknown input observer under model uncertainty and nonlinearity. To solve the design problem, this paper introduces a nonlinear engine dynamic model to catch the internal engine states and an unscented Kalman filter for concurrently performing disturbance and state estimation. The effectiveness is verified through the experiments.