Adaptive model predictive control for hybrid electric propulsion
A hybrid electric propulsion system includes a gas turbine engine having at least one compressor section and at least one turbine section operably coupled to a shaft. The hybrid electric propulsion system includes an electric motor configured to augment rotational power of the shaft of the gas turbine engine. A controller is operable to determine an estimate of hybrid electric propulsion system parameters based on a composite system model and sensor data, determine a model predictive control state and a prediction based on the hybrid electric propulsion system parameters and the composite system model, determine a model predictive control optimization for a plurality of hybrid electric system control effectors based on the model predictive control state and the prediction using a plurality of reduced-order partitions of the composite system model, and actuate the hybrid electric system control effectors based on the model predictive control optimization.
1. A hybrid electric propulsion system comprising:
a gas turbine engine comprising at least one compressor section and at least one turbine section operably coupled to a shaft;
an electric motor configured to augment rotational power of the shaft of the gas turbine engine; and
a controller operable to:
determine an estimate of a plurality of hybrid electric propulsion system parameters based on a composite system model and a plurality of sensor data;
determine a model predictive control state and a prediction based on the hybrid electric propulsion system parameters and the composite system model;
determine a model predictive control optimization for a plurality of hybrid electric system control effectors based on the model predictive control state and the prediction using a plurality of reduced-order partitions of the composite system model; and
actuate the hybrid electric system control effectors based on the model predictive control optimization.
2. The hybrid electric propulsion system of claim 1 , wherein the controller is further configured to update a plurality of composite system model states of the composite system model based on detection of one or more faults.
3. The hybrid electric propulsion system of claim 2 , wherein the controller is further configured to update one or more reduced-order values based on the reduced-order partitions of the composite system model states of the composite system model.
4. The hybrid electric propulsion system of claim 3 , wherein the one or more reduced-order values are reduced-order Jacobian values based on a plurality of Jacobian equations associated with the composite system model.
5. The hybrid electric propulsion system of claim 4 , wherein the reduced-order partitions comprise partitions of a propulsion system model comprising a gas turbine engine model, a mechanical power transmission model, and an electrical power system model that preserve a plurality of dominant states for each partition.
6. The hybrid electric propulsion system of claim 5 , wherein the composite system model comprises the propulsion system model, an optimization objective function, and a plurality of constraints.
7. The hybrid electric propulsion system of claim 6 , wherein the Jacobian equations associated with the composite system model comprise a plurality of model sensitivity matrices that are updated based on the detection of one or more faults.
8. The hybrid electric propulsion system of claim 7 , wherein the model predictive control optimization uses the model sensitivity matrices to determine a set of changes to the hybrid electric system control effectors that optimizes the optimization objective function over a finite time horizon while maintaining the constraints.
9. The hybrid electric propulsion system of claim 1 , further comprising an electric generator configured to extract power from the shaft, wherein the composite system model comprises a plurality of electrical and mechanical physics-based models of at least the gas turbine engine, the electric motor, the electric generator, and one or more mechanical power transmissions.
10. A hybrid electric propulsion system comprising:
a gas turbine engine;
an electrical power system;
a mechanical power transmission operably coupled between the gas turbine engine and the electrical power system;
a plurality of hybrid electric system control effectors operable to control a plurality of states of one or more the gas turbine engine and the electrical power system; and
a controller for controlling the hybrid electric system control effectors based on a model predictive control that is dynamically updated during operation of the hybrid electric propulsion system, the controller operable to:
determine an estimate of a plurality of hybrid electric propulsion system parameters based on a composite system model and a plurality of sensor data;
determine a model predictive control state and a prediction based on the hybrid electric propulsion system parameters and the composite system model;
determine a model predictive control optimization for the hybrid electric system control effectors based on the model predictive control state and the prediction using a plurality of reduced-order partitions of the composite system model; and
actuate the hybrid electric system control effectors based on the model predictive control optimization.
11. The hybrid electric propulsion system of claim 10 , wherein the controller is further configured to update a plurality of composite system model states of the composite system model based on detection of one or more faults and update one or more reduced-order values based on the reduced-order partitions of the composite system model states of the composite system model.
12. The hybrid electric propulsion system of claim 11 , wherein the one or more reduced-order values are reduced-order Jacobian values based on a plurality of Jacobian equations associated with the composite system model, and the reduced-order partitions comprise partitions of a propulsion system model comprising a gas turbine engine model, a mechanical power transmission model, and an electrical power system model that preserve a plurality of dominant states for each partition.
13. The hybrid electric propulsion system of claim 12 , wherein the composite system model comprises the propulsion system model, an optimization objective function, and a plurality of constraints, and the Jacobian equations associated with the composite system model comprise a plurality of model sensitivity matrices that are updated based on the detection of one or more faults.
14. The hybrid electric propulsion system of claim 13 , wherein the model predictive control optimization uses the model sensitivity matrices to determine a set of changes to the hybrid electric system control effectors that optimizes the optimization objective function over a finite time horizon while maintaining the constraints.
15. The hybrid electric propulsion system of claim 10 , wherein the electrical system comprises at least two electric motors, at least two electric generators, and an energy storage system.
16. A method for controlling a hybrid electric propulsion system, the method comprising:
determining, by a controller, an estimate of a plurality of hybrid electric propulsion system parameters based on a composite system model and a plurality of sensor data;
determining, by the controller, a model predictive control state and a prediction based on the hybrid electric propulsion system parameters and the composite system model;
determining, by the controller, a model predictive control optimization for a plurality of hybrid electric system control effectors based on the model predictive control state and the prediction using a plurality of reduced-order partitions of the composite system model; and
actuating, by the controller, the hybrid electric system control effectors based on the model predictive control optimization.
17. The method of claim 16 , further comprising:
updating a plurality of composite system model states of the composite system model based on detection of one or more faults; and
updating one or more reduced-order values based on the reduced-order partitions of the composite system model states of the composite system model.
18. The method of claim 17 , wherein the one or more reduced-order values are reduced-order Jacobian values based on a plurality of Jacobian equations associated with the composite system model, and the reduced-order partitions comprise partitions of a propulsion system model comprising a gas turbine engine model, a mechanical power transmission model, and an electrical power system model that preserve a plurality of dominant states for each partition.
19. The method of claim 18 , wherein the composite system model comprises the propulsion system model, an optimization objective function, and a plurality of constraints, and the Jacobian equations associated with the composite system model comprise a plurality of model sensitivity matrices that are updated based on the detection of one or more faults, wherein the model predictive control optimization uses the model sensitivity matrices to determine a set of changes to the hybrid electric system control effectors that optimizes the optimization objective function over a finite time horizon while maintaining the constraints.