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A method for controlling a gas turbine engine includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model fo

A method for controlling a gas turbine engine includes: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine; at least partially compensating an on-board model for the prediction error data using the model parameter data; generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine; respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; and generating one or more effector signals using the model-based control algorithm.

대표청구항 ▼

1. A method for adaptively controlling a gas turbine engine, comprising: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine and at least one model parameter tha

1. A method for adaptively controlling a gas turbine engine, comprising: generating model parameter data as a function of prediction error data, which model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine and at least one model parameter that accounts for nominal operation of the engine, wherein the engine is configured for an airplane propulsion system;at least partially compensating an on-board model for the prediction error data using the model parameter data;generating model term data using the on-board model, wherein the on-board model includes at least one model term that accounts for the off-nominal operation of the engine, the model term comprises a mathematical equation, and the model term data comprises one or more terms of the mathematical equation;respectively updating one or more model parameters and one or more model terms of a model-based control algorithm with the model parameter data and model term data; andgenerating one or more effector signals using the model-based control algorithm. 2. The method of claim 1, further comprising: providing a processor adapted with at least one of the on-board model and the model-based control algorithm; andproviding the one or more effector signals to one or more engine actuators. 3. The method of claim 1, wherein the off-nominal operation of the engine is indicative of at least one of an abnormal environmental condition in which the airplane propulsion system is operating and engine damage. 4. The method of claim 1, further comprising: generating predicted engine data using the on-board model, which prediction engine data is indicative of modeled system dynamics of the engine; andcomparing measured engine data and the predicted engine data to generate the prediction error data. 5. The method of claim 1, further comprising controlling the engine with the effector signals. 6. The method of claim 1, wherein the generating of the one or more effector signals comprises: receiving one or more goals and one or more limits; andgenerating effector equation data using the model-based control algorithm, which effector equation data is indicative of one or more goal equality equations and one or more limit inequality equations. 7. The method of claim 6, wherein the generating of the one or more effector signals further comprises optimizing the effector equation data to generate the effector signals. 8. The method of claim 7, wherein the effector equation data is optimized using a constrained optimization algorithm. 9. The method of claim 7, wherein the optimizing of the effector equation data further comprises using absolute goal prioritization to weight one or more of the goals. 10. The method of claim 1, wherein the model-base control algorithm comprises one of a 1-step Model Predictive Control and an N-step Model Predictive Control, and wherein the 1-step Model Predictive Control is designed using one of dynamic inversion, backstepping, and feedback linearization. 11. The method of claim 1, further comprising filtering the model parameter data that updates the one or more model parameters of the model-based control algorithm with a low pass filter. 12. The method of claim 1, wherein the model parameter comprises a numerical value. 13. The method of claim 1, wherein the generating of the model parameter data is independent of a fault detection functionality, and the method is operable to operate in a same manner during both the nominal and the off-nominal operation of the engine of the airplane propulsion system. 14. An adaptive control system, comprising: an airplane propulsion system including a gas turbine engine;a control module adapted to generate one or more effector signals using a model-based control algorithm in response to receiving one or more control signals, and to respectively update at least one model parameter and at least one model term of the model-based control algorithm using model parameter data and model term data;an estimator adapted to generate the model parameter data as a function of prediction error data, wherein the model parameter data includes at least one model parameter that accounts for off-nominal operation of the engine of the airplane propulsion system and at least one model parameter that accounts for nominal operation of the engine of the airplane propulsion system; anda modeling module adapted to generate the model term data using an on-board model, and to update the on-board model with the model parameter data to at least partially compensate for the prediction error data, wherein the on-board model includes at least one model term that accounts for off-nominal operation of the engine, the model term comprises a mathematical equation, and the model term data comprises one or more terms of the mathematical equation. 15. The system of claim 14, wherein the estimator is adapted to generate the model parameter data independent of a fault detection functionality, and the control system is operable to operate in a same manner during both the nominal and the off-nominal operation of the engine of the airplane propulsion system. 16. The system of claim 14, further comprising a comparator adapted to provide the prediction error data by comparing measured engine data to predicted engine data. 17. The system of claim 14, wherein the control algorithm is further adapted to optimize goal and limit equations generated by the model-based control algorithm using a constrained optimization algorithm to generate the effector signals. 18. The system of claim 14, wherein the modeling module is further adapted to update the on-board model using the effector signals. 19. The system of claim 14, further comprising a supervisor module that provides supervisor command data to the control module for modifying at least one of the control signals, the model parameters, the model terms and the model-based control algorithm. 20. The system of claim 14, wherein the estimator comprises a Kalman filter. 21. The system of claim 14, wherein the estimator is based on one of a recursive system identification, an optimal estimation, an asymptotic observer and a L1 adaptive control. 22. The system of claim 14, wherein the estimator includes a filter for filtering the model parameter data provided to the control module. 23. The system of claim 14, wherein the model parameter comprises a numerical value.