Machine learned aero-thermodynamic engine inlet condition synthesis

A system forneural network compensated aero-thermodynamic engine model parameter synthesis, the system comprising:

• at least one sensor configured to output sensed values of engine parameters of a gas turbine engine;
• a controller in signal communication with the at least one sensor and configured to control gas turbine engine actuators of the gas turbine engine, wherein the controller comprises:
• an aero-thermodynamic engine model configured to produce real-time model-based estimates of the engine parameters;
• a plurality of machine learning models configured to generate model correction errors based at least on a difference between the real-time model-based estimates of the engine parameters and sensed values of the engine parameters;
• a first comparator configured to produce residuals indicating the difference between the real-time model-based estimates of the engine parameters and the sensed values of the engine parameters;
• an inlet condition estimator configured to iteratively adjust an estimate of inlet conditions based on the residuals to produce selected values of the estimates of the inlet conditions;
• and
• a second comparator configured to compensate the selected values of the estimates of the inlet conditions based on error corrections corresponding to a first aero-thermodynamic transient model for a first operating regime and a second aero-thermodynamic transient model for a second operating regime;
• adaptive control laws configured to produce engine control parameters for control of the gas turbine engine actuators based on the selected values of estimates of the of inlet conditions,
• wherein each machine learning model of the plurality of machine learning models is a machine neural network system, and wherein a first machine learning model of the plurality of machine learning models represents a first aero thermodynamic transient model error for a first operating regime, and a second machine learning model of the plurality of machine learning models represents a second aero-thermodynamic transient model error for a second operating regime that is different from the first operating regime, and
• wherein each machine learning model of the plurality of machine learning models output their respective model correction errors under conditions selected according to the first operating regime and the second operating regime of the first machine learning model and the second machine learning model, respectively.