ADAPTIVE MODEL-BASED CONTROL SYSTEMS AND METHODS FOR CONTROLLING A GAS TURBINE

• US 20040123600A1
• Filed: 11/13/2002
• Published: 07/01/2004
• Est. Priority Date: 11/13/2002
• Status: Active Grant

• Current Assignee: General Electric Company
• Original Assignee: General Electric Company
• Inventors: Brunell, Brent Jerome, Mathews, Harry Kirk Jr., Kumar, Aditya
• Granted Patent: US 6,823,675 B2
• Time in Patent Office: Days
• US Class Current: 60/773
• CPC Class Codes: F02C 9/00 Controlling gas-turbine plants; Controlling fuel supply in air- breathing jet-propulsion plants controlling air intakes F02C7/057; controlling turbines F01D; controlling compressors F04D27/00; controlling in general G05
• CPC Class Codes: F05D 2270/44 active, predictive, or anticipative
• CPC Class Codes: G05B 13/042 in which a parameter or coefficient is automatically adjusted to optimise the performance
• CPC Class Codes: G05B 23/0291 Switching into safety or degraded mode, e.g. protection and supervision after failure

Abstract

Adaptive model-based control systems and methods are described so that performance and/or operability of a gas turbine in an aircraft engine, power plant, marine propulsion, or industrial application can be optimized under normal, deteriorated, faulted, failed and/or damaged operation. First, a model of each relevant system or component is created, and the model is adapted to the engine. Then, if/when deterioration, a fault, a failure or some kind of damage to an engine component or system is detected, that information is input to the model-based control as changes to the model, constraints, objective function, or other control parameters. With all the information about the engine condition, and state and directives on the control goals in terms of an objective function and constraints, the control then solves an optimization so the optimal control action can be determined and taken. This model and control may be updated in real-time to account for engine-to-engine variation, deterioration, damage, faults and/or failures using optimal corrective control action command(s).

Claims

1. An adaptive model-based control method for controlling a gas turbine engine to optimize either performance or operability of the engine, the method comprising:

a) obtaining information about the current state of the engine;

b) updating model data information about the engine in a model in an adaptive model-based control system to reflect the current state of the engine;

c) comparing the information about the current state of the engine with the model data information about the engine in the model;

d) diagnosing sensor, actuator, engine, and subsystem information and passing any updated information to a master mode selector and a reconfigurable control;

e) reconfiguring a correct form of the model, constraints, objective function and predetermined control parameters;

f) determining an optimal corrective control action to take given the current state of the engine, the objective function, and the constraints of the engine;

g) outputting a control command to implement the optimal corrective control action;

h) repeating steps a)-g) as necessary to ensure the objective function of the engine is optimized at all times.

2. The method of claim 1, wherein obtaining information about the current state of the engine comprises obtaining information about at least one of:

the engine, an engine component, an engine system, an engine system component, an engine control system, an engine control system component, a gas path in the engine, gas path dynamics, an actuator, an effector, a controlling device that modifies engine behavior, a sensor, a monitor, a sensing system, a fuel metering system, a fuel delivery system, a lubrication system, a hydraulic system, engine-to-engine variation, deterioration, a mechanical fault, an electrical fault, a chemical fault, a mechanical failure, an electrical failure, a chemical failure, mechanical damage, electrical damage, chemical damage, a system fault, a system failure, and system damage.

3. The method of claim 1, wherein the model in the adaptive model-base control system comprises at least one of:

a physics-based model, a linear system identification model, a nonlinear system identification model, a neural network model, a single simplified parameter model, a multivariable simplified parameter model, a single input single output model, and a multiple input multiple output model.

4. The method of claim 1, wherein the updating step comprises updating at least one of:

a state, a variable, a parameter, a quality parameter, a scalar, an adder, a constraint, an objective function, a limit, an adaptable parameter of the model during steady state operation, and an adaptable parameter of the model during transient operation.

5. The method of claim 1, wherein the updating step comprises adapting the model using at least one of:

a linear estimator, a non-linear estimator, a linear state estimator, a non-linear state estimator, a linear parameter estimator, a non-linear parameter estimator, a linear filter, a non-linear filter, a linear tracking filter, a non-linear tracking filter, linear logic, non-linear logic, linear heuristic logic, non-linear heuristic logic, linear knowledge base, and non-linear knowledge base.

6. The method of claim 1, wherein the diagnosing step comprises using at least one of:

(1) a heuristic, knowledge-based, model-based detection algorithm, and (2) multi-model hypothesis testing.

7. The method of claim 1, wherein the reconfiguring step comprises modifying the model, constraints, objective function and predetermined control parameters using information obtained about the current state of the engine.

8. The method of claim 1, wherein the determining step comprises utilizing an optimizer to determine the optimal corrective control action to take given the current state of the engine, the objective function, and the constraints of the engine.

9. The method of claim 1, wherein the method is performed automatically by a computer.

10. The method of claim 1, wherein the objective function comprises at least one of:

optimize performance of the engine, optimize operability of the engine, maximize thrust, minimize thrust, maximize power, minimize power, maximize electricity use, minimize electricity use, maximize specific fuel consumption, minimize specific fuel consumption, maximize part life, minimize part life, maximize stress, minimize stress, maximize temperatures, minimize temperatures, maximize pressures, minimize pressures, maximize ratios of pressures, minimize ratios of pressures, maximize speed, minimize speed, maximize actuator commands, minimize actuator commands, maximize flows, minimize flows, maximize dollars, minimize dollars, maximize costs of operating the engine, minimize costs of operating the engine, maximize engine run time, minimize engine run time, maximize transient performance, minimize transient performance, maximize steady state performance, minimize steady state performance, maximize engine survivability, minimize engine survivability, manage stall margin, obtain desired references, obey any constraints, and prevent in-flight mishaps.

11. The method of claim 10, wherein the objective function is optimized.

12. The method of claim 11, wherein the objective function is optimized in the presence of at least one of:

deterioration, fault, failure and damage.

13. An adaptable model-based control system for controlling a gas turbine engine to optimize either performance or operability of the engine, the system comprising:

a) a means for obtaining information about the current state of the engine;

b) a means for updating model data information about the engine in a model in an adaptive model-based control system to reflect the current state of the engine;

c) a means for comparing the information about the current state of the engine with the model data information about the engine in the model;

d) a means for diagnosing sensor, actuator, engine, and subsystem information and passing any updated information to a master mode selector and a reconfigurable control;

e) a means for reconfiguring a correct form of the model, constraints, objective function and predetermined control parameters;

f) a means for determining an optimal corrective control action to take given the current state of the engine, the objective function, and the constraints of the engine;

g) a means for outputting a control command to implement the optimal corrective control action;

h) a means for repeating steps a)-g) as necessary to ensure the objective function of the engine is optimized at all times.

14. The system of claim 13, wherein the means for obtaining information about the current state of the engine comprises obtaining information about at least one of:

the engine, an engine component, an engine system, an engine system component, an engine control system, an engine control system component, a gas path in the engine, gas path dynamics, an actuator, an effector, a controlling device that modifies engine behavior, a sensor, a monitor, a sensing system, a fuel metering system, a fuel delivery system, a lubrication system, a hydraulic system, engine-to-engine variation, deterioration, a mechanical fault, an electrical fault, a chemical fault, a mechanical failure, an electrical failure, a chemical failure, mechanical damage, electrical damage, chemical damage, a system fault, a system failure, and system damage.

15. The system of claim 13, wherein the model in the adaptive model-base control system comprises at least one of:

a physics-based model, a linear system identification model, a nonlinear system identification model, a neural network model, a single simplified parameter model, a multivariable simplified parameter model, a single input single output model, and a multiple input multiple output model.

16. The system of claim 13, wherein the means for updating model data information comprises a means for modifying at least one of:

a state, a variable, a parameter, a quality parameter, a scalar, an adder, a constraint, an objective function, a limit, an adaptable parameter of the model during steady state operation, and an adaptable parameter of the model during transient operation.

17. The system of claim 13, wherein the means for updating model data information comprises a means for adapting the model using at least one of:

a linear estimator, a non-linear estimator, a linear state estimator, a non-linear state estimator, a linear parameter estimator, a non-linear parameter estimator, a linear filter, a non-linear filter, a linear tracking filter, a non-linear tracking filter, linear logic, non-linear logic, linear heuristic logic, non-linear heuristic logic, linear knowledge base, and non-linear knowledge base.

18. The system of claim 13, wherein the means for diagnosing sensor, actuator, engine, and subsystem information and passing any updated information to a master mode selector and a reconfigurable control comprises using at least one of:

(1) a heuristic, knowledge-based, model-based detection algorithm, and (2) multi-model hypothesis testing.

19. The system of claim 13, wherein the means for reconfiguring a correct form of the model, constraints, objective function and other relevant control parameters comprises modifying the model, constraints, objective function and predetermined control parameters using information obtained about the current state of the engine.

20. The system of claim 13, wherein the means for determining the optimal corrective control action to take comprises a means for utilizing an optimizer to determine the optimal corrective control action to take given the current state of the engine, the objective function, and the constraints of the engine.

21. The system of claim 13, wherein the system utilizes a computer to perform each operation automatically.

22. The system of claim 13, wherein the objective function comprises at least one of:

optimize performance of the engine, optimize operability of the engine, maximize thrust, minimize thrust, maximize power, minimize power, maximize electricity use, minimize electricity use, maximize specific fuel consumption, minimize specific fuel consumption, maximize part life, minimize part life, maximize stress, minimize stress, maximize temperatures, minimize temperatures, maximize pressures, minimize pressures, maximize ratios of pressures, minimize ratios of pressures, maximize speed, minimize speed, maximize actuator commands, minimize actuator commands, maximize flows, minimize flows, maximize dollars, minimize dollars, maximize costs of operating the engine, minimize costs of operating the engine, maximize engine run time, minimize engine run time, maximize transient performance, minimize transient performance, maximize steady state performance, minimize steady state performance, maximize engine survivability, minimize engine survivability, manage stall margin, obtain desired references, obey any constraints, and prevent in-flight mishaps.

23. The system of claim 22, wherein the objective function is optimized.

24. The system of claim 23, wherein the objective function is optimized in the presence of at least one of:

deterioration, fault, failure and damage.

25. An adaptable model-based control system capable of controlling a gas turbine engine to optimize either performance or operability of the engine, the control system comprising:

at least one model capable of representing system behavior;

at least one estimator capable of determining a current state of the engine and adapting the model for engine-to-engine variation and deterioration;

at least one fault diagnostic capable of detecting and classifying faults and damage and providing a probability of faults;

at least one reconfigurable model-based control capable of utilizing an optimization to control engine manipulated variables to ensure optimal operation of the engine within constrained operating space; and

a master mode selector capable of modifying at least one of;

an objective function, a constraint, a reference, a model and a model structure using information from at least one of;

an operator, a supervisory control, and fault diagnostics.

26. The control system of claim 25, wherein the system behavior comprises at least one of steady state behavior and transient behavior.

27. The control system of claim 25, wherein input to the model comprises at least one manipulated variable.

28. The control system of claim 25, wherein output from the model comprises at least one of:

a sensed parameter and an unsensed parameter.

29. The control system of claim 25, wherein input to the estimator comprises at least one of:

a previous control action, a sensed output, a reference, a previous parameter estimate, and a previous state estimate.

30. The control system of claim 25, wherein output from the estimator comprises at least one of:

an adapted model parameter, an estimate covariance, and a state estimate.

31. The control system of claim 25, wherein input to the fault diagnostic comprises at least one of:

an innovation, a previous control action, a sensed variable, a reference, a state estimate, a parameter estimate, and a database of models.

32. The control system of claim 25, wherein output from the fault diagnostic comprises fault detection, isolation and identification information.

33. The control system of claim 25, wherein input to the reconfigurable model-based control comprises at least one of:

a reference, a state estimate, a parameter estimate, a covariance, a constraint, an objective function, and an objective function weight.

34. The control system of claim 25, wherein output from the reconfigurable model-based control comprises at least one control command.

35. The control system of claim 25, wherein the estimator obtains information about the current state of the engine.

36. The control system of claim 35, wherein the information about the current state of the engine comprises information about at least one of:

the engine, an engine component, an engine system, an engine system component, an engine control system, an engine control system component, a gas path in the engine, gas path dynamics, an actuator, an effector, a controlling device that modifies engine behavior, a sensor, a monitor, a sensing system, a fuel metering system, a fuel delivery system, a lubrication system, a hydraulic system, engine-to-engine variation, deterioration, a mechanical fault, an electrical fault, a chemical fault, a mechanical failure, an electrical failure, a chemical failure, mechanical damage, electrical damage, chemical damage, a system fault, a system failure, and system damage.

37. The control system of claim 25, wherein the model comprises at least one of:

a physics-based model, a linear system identification model, a nonlinear system identification model, a neural network model, a single simplified parameter model, a multivariable simplified parameter model, a single input single output model, and a multiple input multiple output model.

38. The control system of claim 25, wherein the master mode selector updates the model data information by modifying at least one of:

a state, a variable, a parameter, a quality parameter, a scalar, an adder, a constraint, an objective function, a limit, an adaptable parameter of the model during steady state operation, and an adaptable parameter of the model during transient operation.

39. The control system of claim 25, wherein the estimator updates the model data information by adapting the model using at least one of:

a linear estimator, a non-linear estimator, a linear state estimator, a non-linear state estimator, a linear parameter estimator, a non-linear parameter estimator, a linear filter, a non-linear filter, a linear tracking filter, a non-linear tracking filter, linear logic, non-linear logic, linear heuristic logic, non-linear heuristic logic, linear knowledge base, and non-linear knowledge base.

40. The control system of claim 25, wherein the fault diagnostic comprises at least one diagnostic fault detection algorithm.

41. The control system of claim 25, wherein the fault diagnostic diagnoses sensor, actuator, engine, and subsystem information and passes any updated information to a master mode selector and a reconfigurable control using at least one of:

(1) a heuristic, knowledge-based, model-based detection algorithm, and (2) multi-model hypothesis testing.

42. The control system of claim 25, wherein the reconfigurable model-based control determines an optimized corrective control action to take by utilizing an optimizer, given the current state of the engine, the objective function, and the constraints of the engine.

43. The control system of claim 25, wherein the master mode selector reconfigures a correct form of the model, constraints, objective function and other relevant control parameters by modifying the model, constraints, objective function and predetermined control parameters using information obtained about the current state of the engine.

44. The control system of claim 25, wherein the system is automated by a computer.

45. The control system of claim 25, wherein the objective function comprises at least one of:

optimize performance of the engine, optimize operability of the engine, maximize thrust, minimize thrust, maximize power, minimize power, maximize electricity use, minimize electricity use, maximize specific fuel consumption, minimize specific fuel consumption, maximize part life, minimize part life, maximize stress, minimize stress, maximize temperatures, minimize temperatures, maximize pressures, minimize pressures, maximize ratios of pressures, minimize ratios of pressures, maximize speed, minimize speed, maximize actuator commands, minimize actuator commands, maximize flows, minimize flows, maximize dollars, minimize dollars, maximize costs of operating the engine, minimize costs of operating the engine, maximize engine run time, minimize engine run time, maximize transient performance, minimize transient performance, maximize steady state performance, minimize steady state performance, maximize engine survivability, minimize engine survivability, manage stall margin, obtain desired references, obey any constraints, and prevent in-flight mishaps.

46. The control system of claim 45, wherein the objective function is optimized.

47. The control system of claim 46, wherein the objective function is optimized in the presence of at least one of:

deterioration, fault, failure, and damage.